QA and Performance Testing Engineering Lead

🏢 Summary: The role focuses on leading AI-driven transformation of enterprise QA by implementing advanced automation frameworks, predictive quality insights, and modern testing strategies. It aims to modernize quality engineering across complex ERP, e-commerce, and cloud-native environments while integrating continuous testing into CI/CD pipelines. The position drives innovation, efficiency, and strategic value through AI-enabled QA practices. 🗂️ Requirements: 8+ years of QA experience, Hands-on experience with automation frameworks, Experience with AI-driven testing tools, Experience with large-scale enterprise systems, Ability to implement AI-assisted QA and predictive testing, Experience with continuous testing and CI/CD integration, Experience with ERP, e-commerce, and cloud-native applications, Ability to define QA metrics and predictive quality insights, Experience leading QA modernization initiatives 📃 Skills: AI, Automation, CI/CD, ERP, E-commerce, Cloud, PredictiveAnalytics, Playwright, MCP, Cursor, SDLC, RegressionTesting 🏢 Description: Project overview This project focuses on modernizing enterprise-level QA through AI-driven frameworks, predictive quality insights, and automation strategies. The initiative brings innovation to complex integrated environments, moving QA toward a strategic, value-driven function while improving efficiency and delivery accuracy. Team You will collaborate with enterprise QA specialists, automation engineers, architects, and client stakeholders. The team works in a cross-functional environment with a strong focus on continuous improvement, knowledge sharing, and modern QA practices. Position overview We are looking for an Enterprise QA Innovation Lead who will drive AI-enabled transformation across enterprise QA engagements and elevate quality engineering to a strategic, innovation-focused discipline. Technology stack AI-assisted testing tools, automation frameworks, ERP systems, e-commerce platforms, cloud native applications, CI/CD pipelines, predictive analytics, and modern QA toolsets Responsibilities Lead the design and implementation of AI-driven QA frameworks that optimize test coverage, detect anomalies, and generate predictive quality insights Drive process innovation across enterprise QA engagements, identifying opportunities to reduce manual effort, accelerate delivery, and standardize QA practices Collaborate with enterprise clients, internal QA teams, and engineering teams to integrate modern QA methodologies into the SDLC, including shift left, continuous testing, and CI/CD pipelines Develop and deploy advanced automation strategies, leveraging AI-assisted test generation and intelligent regression testing for ERP, e-commerce, and cloud native applications Define metrics and analytics frameworks to monitor QA efficiency, automation ROI, defect trends, and predictive quality indicators Evaluate and introduce emerging QA technologies, AI-driven tools, and best practices for enterprise software quality Act as a strategic innovation partner to enterprise clients, shaping QA approaches for complex environments and delivering measurable business value Lead QA modernization initiatives across enterprise accounts, focusing on process efficiency, automation, and predictive QA adoption Requirements 8 years of QA experience, including hands-on experience with automation frameworks, AI-driven testing, and large-scale enterprise systems Demonstrated ability to implement AI-assisted QA tools, predictive QA, and continuous testing strategies Background in optimizing QA processes for ERP, e-commerce, and cloud native applications Strong analytical and problem-solving skills with experience defining QA metrics and predictive insights Experience collaborating with enterprise clients and internal teams to implement innovative QA solutions Strong communication skills with the ability to convey technical innovation and strategic QA improvements to stakeholders Nice to have Experience with AI testing tools such as Playwright MCP, Cursor, or similar Background in QA digital transformation initiatives

🏢 Summary: QA Lead role responsible for defining and executing the quality strategy, leading the QA team, and shaping test architecture for new products built from scratch. The position combines technical leadership in test automation with operational support and close collaboration with product and business stakeholders. The role has a direct impact on technology choices, testing standards, and overall engineering excellence. 🗂️ Requirements: Proven experience in leading QA teams, Advanced knowledge of test automation, Strong understanding of SDLC, Experience in designing test architecture, Hands-on experience with regression, smoke, and manual testing, Experience working with cross-functional product teams, Ability to select and evaluate testing technologies 📃 Skills: Playwright, Selenium, Appium, Java, SDLC, AI, Claude, Cursor, Copilot, Android, iOS 🏢 Description: O roli: W naszej organizacji rola QA Lead to rola techniczna. Szukamy eksperta, który bierze pełną odpowiedzialność za strategię jakości i kierunek rozwoju technicznego zespołu QA. Twoim głównym obszarem działania jest technologia: architektura testów, jakość kodu automatyzacji i techniczne standardy pracy oraz wykorzystanie AI do podnoszenia produktywności i jakości w pracy. Staniesz się kluczowym partnerem technicznym dla biznesu i inżynierii, a Twoim głównym celem będzie budowanie kultury odpowiedzialności i decyzyjności, w której QA ma realny wpływ na architekturę rozwiązań. Wierzymy, że lider techniczny to osoba, która usuwa przeszkody, podnosi poprzeczkę technicznie i wyznacza standardy. Twoje kluczowe zadania: Strategia i przywództwo techniczne: masz decydujący wpływ na wybór technologii oraz architekturę testów. Pracujemy w oparciu o Playwright (w TypeScript), Selenium i Appium (w Javie). Od Ciebie oczekujemy biegłości w tych technologiach oraz świadomych decyzji architektonicznych dotyczących frameworków testowych. Hands-on engineering: aktywnie uczestniczysz w projektowaniu i przeglądach kodu testów automatycznych, samodzielnie weryfikujesz jakość rozwiązań technicznych i wyznaczasz wzorce, które zespół może realnie naśladować. AI i automatyzacja procesów QA: wyznaczasz kierunek wykorzystania AI w testowaniu: od generowania i utrzymania testów z użyciem Claude Code, Cursora lub GitHub Copilota, przez agentowe workflow do analizy regresji i triage bugów, po automatyzację rutynowych zadań QA (np. przygotowanie danych testowych, raportowanie, klasyfikacja defektów). Definiujesz standardy, które testy warto pisać "po ludzku", a które delegować do agentów, i dbasz, by zespół realnie podnosił produktywność dzięki tym narzędziom, bez utraty jakości. Budowanie i rozwój: odpowiadasz za techniczną rekrutację nowych talentów oraz wspieranie inżynierów QA w rozwoju ich kompetencji technicznych: code review, mentoring techniczny, podnoszenie poziomu automatyzacji. Zarządzanie operacyjne i wsparcie: jesteś liderem technicznym, który aktywnie wspiera zespoły w momentach dużego obciążenia. Włączasz się merytorycznie, usuwasz blokady techniczne i dbasz o płynność procesów testowych. Partnerstwo z produktem i biznesem: współpracujesz bezpośrednio z Product Managerami oraz klientami wewnętrznymi, ustalając zakres odpowiedzialności QA oraz dbając, by finalny kształt funkcjonalności uwzględniał feedback od deweloperów i testerów. Engineering Excellence i bezpieczeństwo: nadzorujesz jakość poprzez testy regresyjne, smoke-testy i testy manualne, a także wyznaczasz standardy techniczne dla całego procesu zapewniania jakości. Współpraca Cross-functional: działasz jako partner techniczny dla pozostałych zespołów produktowych, wspólnie kształtujecie standardy pracy. Nasze wymagania: Ekspercki warsztat techniczny (kluczowe): p osiadasz zaawansowaną, praktyczną wiedzę z zakresu testów automatycznych, w szczególności w Playwright z TypeScript oraz Selenium/Appium z Javą. Czytasz, piszesz i recenzujesz kod testów na poziomie eksperckim. Rozumiesz cykl życia oprogramowania (SDLC), wzorce projektowe w automatyzacji oraz architekturę frameworków testowych. Decyzyjność architektoniczna: Masz doświadczenie w podejmowaniu decyzji o wyborze narzędzi, projektowaniu skalowalnych rozwiązań testowych i potrafisz uzasadnić swoje wybory technicznie. Praktyczne doświadczenie z AI w pracy inżynierskiej: korzystasz na co dzień z narzędzi typu Claude Code, Cursor lub GitHub Copilot. Nie do generowania fragmentów, ale do realnej pracy nad kodem testów, refaktoryzacji, debugowania i tworzenia oprzyrządowania. Wiesz, gdzie AI realnie skraca czas pracy QA, a gdzie wprowadza ryzyko (kruche testy, fałszywe poczucie pokrycia, halucynowane API). Mile widziane doświadczenie z agentowymi workflow lub integracjami przez MCP. Doświadczenie w prowadzeniu technicznym zespołu QA: Masz udokumentowane sukcesy w prowadzeniu zespołu QA od strony technicznej: wyznaczanie kierunku, code review, mentoring techniczny oraz naturalną chęć wspierania testerów w rozwoju kompetencji inżynierskich. Budowanie silnych zespołów: p otrafisz rekrutować talenty technicznie i nie boisz się zatrudniać osób, które w specyficznych obszarach posiadają większą wiedzę od Ciebie. Łączenie światów: u miejętnie balansujesz między potrzebami biznesowymi a możliwościami technicznymi, znajdując optymalne rozwiązania dla produktu. Kultura feedbacku i nauki: masz odwagę przyznawać się do błędów i traktujesz je jako cenną lekcję, wyciągając wnioski, które realnie usprawniają przyszłe procesy. Benefity: Nowoczesny sprzęt: MacBook Pro lub Air, dodatkowy monitor oraz telefony testowe ( Android i iOS ). Wsparcie AI: Pełne finansowanie narzędzi automatyzujących pracę ( Claude Code, Cursor, Windsurf, GitHub Copilot ). Ergonomiczne stanowisko: Elektrycznie regulowane biurko oraz wygodne krzesło. Prywatna opieka i ubezpieczenie: Pakiet medyczny oraz NNW dla Ciebie, Twojej rodziny lub partnera. Sport i rekreacja: Karta MultiSport dla Ciebie i bliskiej osoby. Pełne wyżywienie: Codzienne obiady na koszt firmy , świeże owoce oraz doskonała kawa. Rozwój merytoryczny: Indywidualny budżet szkoleniowy, opieka mentora oraz techniczna biblioteczka. Kultura i integracja: Jasne rozmowy o wynikach, wyjazdy integracyjne i cykliczne spotkania zespołu. Komfortowe biuro: Klimatyzowana przestrzeń w Centrum Krakowa (10 min od Dworca Głównego) z parkingiem rowerowym i prysznicami. Wsparcie relokacyjne: Pomoc finansowa przy przeprowadzce do Krakowa. Elastyczność: Wybór między B2B a Umową o pracę , model hybrydowy oraz start pracy między 7:00 a 10:00 . Tańsza księgowość: Zniżka na usługi inFakt.pl w wysokości 300 zł netto miesięcznie . Aplikuj i twórz z nami jakość aplikacji nr 1 na polskim rynku!

🏢 Summary: Lead AI Testing Engineer role focused on building and driving QA strategy for AI-driven, knowledge graph, ontology, and RAG-based LLM systems. The position involves automating evaluation frameworks, validating graph and ontology layers, integrating testing into CI/CD pipelines, and ensuring end-to-end quality across backend, frontend, and AI components. The offer includes flexible remote work within Poland, professional development programs, certifications, and comprehensive benefits. 🗂️ Requirements: 8+ years of experience in software development, test automation and DevOps, Python test automation with pytest or equivalent, Experience with AI/ML library integration, Knowledge Graph or Data Ontology testing experience, OWL2, RDF, SPARQL, SHACL validation, GraphDB experience, SQL data validation, Cypher query language, REST API testing, Frontend test automation with Playwright or equivalent, Experience testing RAG-based Gen AI / LLM applications, Knowledge of Gen AI / LLM evaluation metrics, Automation framework development experience, CI/CD pipeline integration, Agile delivery experience, Git, Azure DevOps or JIRA, English proficiency at B2 level or higher, Hands-on experience with coding agents and agentic development 📃 Skills: Python, pytest, OWL2, RDF, SPARQL, SHACL, GraphDB, SQL, Cypher, REST, Playwright, Git, Azure, JIRA, Jenkins, Postman, Bruno, LLM, RAG, GenAI, CI/CD 🏢 Description: We are looking for a Lead AI Testing Engineer to drive the quality strategy for AI-driven and data migration initiatives spanning knowledge graphs, ontologies and RAG-based LLM features. You will define and execute QA practices across data ingestion, graph layers, rule evaluation engines and AI/LLM components while building automation frameworks that scale evaluation across complex, data-heavy systems. Responsibilities Design and automate evaluation of RAG-based and LLM-driven features including grounding, answer accuracy, determinism/reproducibility, precision, recall and hallucination rate Build test harnesses to scale evaluation beyond human-in-the-loop processes Create data-driven test suites for underwriting rules and validate rule execution via SPARQL evaluator, arithmetic/threshold logic and multi-condition scenarios Verify ontology schema correctness and instance accuracy against source data, perform reasoner consistency checks and SHACL validation Define and drive end-to-end QA strategy covering data ingestion, ontology/graph layer, rule evaluation engine, AI/LLM layer and integration with underwriting solutions Establish quality gates, acceptance criteria and test coverage models Maintain automation test suites across knowledge graph, data ontology layer, backend services and frontend layers Validate end-to-end flows across ontology, rule engine and decision output, perform contract testing and cross-system data consistency validation Integrate test suites into CI/CD pipelines and define release quality gates Champion automation-first and shift-left quality practices Leverage agentic AI and Gen AI tooling in testing and framework development Requirements 8+ years of experience in software development, testing automation and DevOps Proficiency in Python test automation using pytest or equivalent, scripting and AI/ML library integration Expertise in testing Knowledge Graph or Data Ontology solutions using OWL2, RDF and SPARQL Skills in SHACL validation and graph databases such as GraphDB, including ontology validation, entity/relationship integrity, reasoner consistency checks and graph query correctness Proven ability to define and drive QA strategy independently for complex data-heavy systems, including building automation frameworks and implementing quality gates Skills in data validation using SQL and graph query languages such as SPARQL and Cypher, along with understanding of deterministic vs probabilistic systems Background in backend and API testing (REST) covering data validation, integration and E2E testing Proficiency in frontend web application test automation using Playwright or equivalent Python-compatible framework Hands-on experience using coding agents and agentic development daily Demonstrated experience testing and evaluating RAG-based Gen AI / LLM applications including grounding, answer accuracy and hallucination/determinism checks Applied knowledge of Gen AI / LLM evaluation frameworks and metrics such as precision, recall, criteria recall and efficiency, with proven ability to automate Gen AI/RAG evaluation at scale Experience in Agile delivery using Git and Azure DevOps or JIRA English proficiency at B2 level or higher Nice to have Skills in semantic search testing and evaluation Familiarity with Vector Database integration and retrieval validation Background in data science covering ML concepts, data pipelines or data engineering collaboration Proficiency in API tooling such as Postman and Bruno Experience with quality-gate implementation in CI/CD pipelines such as Azure DevOps or Jenkins We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: Lead AI Testing Engineer role focused on defining and automating QA strategy for AI-driven, knowledge graph, ontology, and RAG-based LLM systems. The position involves building scalable test frameworks, validating graph and ontology data, and integrating automated quality gates into CI/CD pipelines across backend, frontend, and AI layers. Candidates should have strong expertise in Python automation, semantic technologies, GenAI evaluation, and end-to-end testing for complex data-heavy systems. 🗂️ Requirements: 8+ years in software development, test automation and DevOps, Proficiency in Python test automation, Experience with pytest or equivalent frameworks, Expertise in OWL2, RDF and SPARQL, Experience testing Knowledge Graph or Data Ontology solutions, Skills in SHACL validation, Experience with GraphDB or graph databases, Ability to define QA strategy for complex data-heavy systems, Experience building automation frameworks and quality gates, Skills in SQL, SPARQL and Cypher, Experience with REST API testing, Experience with frontend automation using Playwright or equivalent, Hands-on experience with coding agents and agentic development, Experience testing RAG-based GenAI and LLM applications, Knowledge of GenAI/LLM evaluation metrics, Ability to automate GenAI/RAG evaluation at scale, Experience with Git and Azure DevOps or JIRA, English proficiency at B2 level or higher 📃 Skills: Python, pytest, OWL2, RDF, SPARQL, SHACL, GraphDB, SQL, Cypher, REST, Playwright, Git, JIRA, Azure, Jenkins, Postman, Bruno, LLM, RAG, GenAI, CI/CD 🏢 Description: We are looking for a Lead AI Testing Engineer to drive the quality strategy for AI-driven and data migration initiatives spanning knowledge graphs, ontologies and RAG-based LLM features. You will define and execute QA practices across data ingestion, graph layers, rule evaluation engines and AI/LLM components while building automation frameworks that scale evaluation across complex, data-heavy systems. Responsibilities Design and automate evaluation of RAG-based and LLM-driven features including grounding, answer accuracy, determinism/reproducibility, precision, recall and hallucination rate Build test harnesses to scale evaluation beyond human-in-the-loop processes Create data-driven test suites for underwriting rules and validate rule execution via SPARQL evaluator, arithmetic/threshold logic and multi-condition scenarios Verify ontology schema correctness and instance accuracy against source data, perform reasoner consistency checks and SHACL validation Define and drive end-to-end QA strategy covering data ingestion, ontology/graph layer, rule evaluation engine, AI/LLM layer and integration with underwriting solutions Establish quality gates, acceptance criteria and test coverage models Maintain automation test suites across knowledge graph, data ontology layer, backend services and frontend layers Validate end-to-end flows across ontology, rule engine and decision output, perform contract testing and cross-system data consistency validation Integrate test suites into CI/CD pipelines and define release quality gates Champion automation-first and shift-left quality practices Leverage agentic AI and Gen AI tooling in testing and framework development Requirements 8+ years of experience in software development, testing automation and DevOps Proficiency in Python test automation using pytest or equivalent, scripting and AI/ML library integration Expertise in testing Knowledge Graph or Data Ontology solutions using OWL2, RDF and SPARQL Skills in SHACL validation and graph databases such as GraphDB, including ontology validation, entity/relationship integrity, reasoner consistency checks and graph query correctness Proven ability to define and drive QA strategy independently for complex data-heavy systems, including building automation frameworks and implementing quality gates Skills in data validation using SQL and graph query languages such as SPARQL and Cypher, along with understanding of deterministic vs probabilistic systems Background in backend and API testing (REST) covering data validation, integration and E2E testing Proficiency in frontend web application test automation using Playwright or equivalent Python-compatible framework Hands-on experience using coding agents and agentic development daily Demonstrated experience testing and evaluating RAG-based Gen AI / LLM applications including grounding, answer accuracy and hallucination/determinism checks Applied knowledge of Gen AI / LLM evaluation frameworks and metrics such as precision, recall, criteria recall and efficiency, with proven ability to automate Gen AI/RAG evaluation at scale Experience in Agile delivery using Git and Azure DevOps or JIRA English proficiency at B2 level or higher Nice to have Skills in semantic search testing and evaluation Familiarity with Vector Database integration and retrieval validation Background in data science covering ML concepts, data pipelines or data engineering collaboration Proficiency in API tooling such as Postman and Bruno Experience with quality-gate implementation in CI/CD pipelines such as Azure DevOps or Jenkins We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: Lead AI Testing Engineer role focused on defining and automating QA strategies for AI-driven systems, knowledge graphs, ontologies and RAG-based LLM applications. The position involves building scalable test frameworks, validating data-heavy systems, integrating quality gates into CI/CD pipelines and ensuring end-to-end quality across backend, frontend and AI layers. Candidates will work with graph technologies, GenAI evaluation frameworks and automation-first testing practices. 🗂️ Requirements: 8+ years of experience in software development, test automation and DevOps, Python test automation with pytest or equivalent, Experience with AI/ML library integration, Knowledge Graph or Data Ontology testing experience, OWL2, RDF, SPARQL, SHACL validation, GraphDB experience, QA strategy design for complex data-heavy systems, SQL and graph query language validation, Cypher, Backend and REST API testing, Frontend test automation with Playwright or equivalent, Experience with coding agents and agentic development, RAG-based Gen AI / LLM testing and evaluation, Gen AI / LLM evaluation frameworks and metrics, Automation of Gen AI/RAG evaluation at scale, Agile delivery experience, Git, Azure DevOps or JIRA, English proficiency B2 or higher 📃 Skills: Python, pytest, OWL2, RDF, SPARQL, SHACL, GraphDB, SQL, Cypher, REST, Playwright, RAG, LLM, Git, Azure, JIRA, Postman, Bruno, Jenkins, CI/CD 🏢 Description: We are looking for a Lead AI Testing Engineer to drive the quality strategy for AI-driven and data migration initiatives spanning knowledge graphs, ontologies and RAG-based LLM features. You will define and execute QA practices across data ingestion, graph layers, rule evaluation engines and AI/LLM components while building automation frameworks that scale evaluation across complex, data-heavy systems. Responsibilities Design and automate evaluation of RAG-based and LLM-driven features including grounding, answer accuracy, determinism/reproducibility, precision, recall and hallucination rate Build test harnesses to scale evaluation beyond human-in-the-loop processes Create data-driven test suites for underwriting rules and validate rule execution via SPARQL evaluator, arithmetic/threshold logic and multi-condition scenarios Verify ontology schema correctness and instance accuracy against source data, perform reasoner consistency checks and SHACL validation Define and drive end-to-end QA strategy covering data ingestion, ontology/graph layer, rule evaluation engine, AI/LLM layer and integration with underwriting solutions Establish quality gates, acceptance criteria and test coverage models Maintain automation test suites across knowledge graph, data ontology layer, backend services and frontend layers Validate end-to-end flows across ontology, rule engine and decision output, perform contract testing and cross-system data consistency validation Integrate test suites into CI/CD pipelines and define release quality gates Champion automation-first and shift-left quality practices Leverage agentic AI and Gen AI tooling in testing and framework development Requirements 8+ years of experience in software development, testing automation and DevOps Proficiency in Python test automation using pytest or equivalent, scripting and AI/ML library integration Expertise in testing Knowledge Graph or Data Ontology solutions using OWL2, RDF and SPARQL Skills in SHACL validation and graph databases such as GraphDB, including ontology validation, entity/relationship integrity, reasoner consistency checks and graph query correctness Proven ability to define and drive QA strategy independently for complex data-heavy systems, including building automation frameworks and implementing quality gates Skills in data validation using SQL and graph query languages such as SPARQL and Cypher, along with understanding of deterministic vs probabilistic systems Background in backend and API testing (REST) covering data validation, integration and E2E testing Proficiency in frontend web application test automation using Playwright or equivalent Python-compatible framework Hands-on experience using coding agents and agentic development daily Demonstrated experience testing and evaluating RAG-based Gen AI / LLM applications including grounding, answer accuracy and hallucination/determinism checks Applied knowledge of Gen AI / LLM evaluation frameworks and metrics such as precision, recall, criteria recall and efficiency, with proven ability to automate Gen AI/RAG evaluation at scale Experience in Agile delivery using Git and Azure DevOps or JIRA English proficiency at B2 level or higher Nice to have Skills in semantic search testing and evaluation Familiarity with Vector Database integration and retrieval validation Background in data science covering ML concepts, data pipelines or data engineering collaboration Proficiency in API tooling such as Postman and Bruno Experience with quality-gate implementation in CI/CD pipelines such as Azure DevOps or Jenkins We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: Lead AI Testing Engineer role focused on building and driving QA strategy for AI-driven, knowledge graph, ontology and RAG-based LLM systems. The position involves developing scalable automation frameworks, validating complex data and rule engines, and integrating AI evaluation into CI/CD pipelines. The role also includes end-to-end testing across backend, frontend, graph databases and GenAI applications. 🗂️ Requirements: 8+ years of experience in software development, test automation and DevOps, Python test automation with pytest or equivalent, Experience with AI/ML library integration, Knowledge Graph or Data Ontology testing experience, OWL2 knowledge, RDF knowledge, SPARQL expertise, SHACL validation skills, GraphDB experience, QA strategy development for complex data-heavy systems, SQL data validation, Cypher query language knowledge, Backend and REST API testing, Frontend test automation with Playwright or equivalent, Experience with coding agents and agentic development, RAG-based GenAI/LLM testing experience, GenAI/LLM evaluation metrics and frameworks knowledge, Automation of GenAI/RAG evaluation at scale, Agile delivery experience, Git experience, Azure DevOps or JIRA experience, English proficiency at B2 level or higher 📃 Skills: Python, pytest, OWL2, RDF, SPARQL, SHACL, GraphDB, SQL, Cypher, REST, Playwright, Git, Azure, JIRA, Jenkins, Postman, Bruno, RAG, LLM, GenAI, CI/CD 🏢 Description: We are looking for a Lead AI Testing Engineer to drive the quality strategy for AI-driven and data migration initiatives spanning knowledge graphs, ontologies and RAG-based LLM features. You will define and execute QA practices across data ingestion, graph layers, rule evaluation engines and AI/LLM components while building automation frameworks that scale evaluation across complex, data-heavy systems. Responsibilities Design and automate evaluation of RAG-based and LLM-driven features including grounding, answer accuracy, determinism/reproducibility, precision, recall and hallucination rate Build test harnesses to scale evaluation beyond human-in-the-loop processes Create data-driven test suites for underwriting rules and validate rule execution via SPARQL evaluator, arithmetic/threshold logic and multi-condition scenarios Verify ontology schema correctness and instance accuracy against source data, perform reasoner consistency checks and SHACL validation Define and drive end-to-end QA strategy covering data ingestion, ontology/graph layer, rule evaluation engine, AI/LLM layer and integration with underwriting solutions Establish quality gates, acceptance criteria and test coverage models Maintain automation test suites across knowledge graph, data ontology layer, backend services and frontend layers Validate end-to-end flows across ontology, rule engine and decision output, perform contract testing and cross-system data consistency validation Integrate test suites into CI/CD pipelines and define release quality gates Champion automation-first and shift-left quality practices Leverage agentic AI and Gen AI tooling in testing and framework development Requirements 8+ years of experience in software development, testing automation and DevOps Proficiency in Python test automation using pytest or equivalent, scripting and AI/ML library integration Expertise in testing Knowledge Graph or Data Ontology solutions using OWL2, RDF and SPARQL Skills in SHACL validation and graph databases such as GraphDB, including ontology validation, entity/relationship integrity, reasoner consistency checks and graph query correctness Proven ability to define and drive QA strategy independently for complex data-heavy systems, including building automation frameworks and implementing quality gates Skills in data validation using SQL and graph query languages such as SPARQL and Cypher, along with understanding of deterministic vs probabilistic systems Background in backend and API testing (REST) covering data validation, integration and E2E testing Proficiency in frontend web application test automation using Playwright or equivalent Python-compatible framework Hands-on experience using coding agents and agentic development daily Demonstrated experience testing and evaluating RAG-based Gen AI / LLM applications including grounding, answer accuracy and hallucination/determinism checks Applied knowledge of Gen AI / LLM evaluation frameworks and metrics such as precision, recall, criteria recall and efficiency, with proven ability to automate Gen AI/RAG evaluation at scale Experience in Agile delivery using Git and Azure DevOps or JIRA English proficiency at B2 level or higher Nice to have Skills in semantic search testing and evaluation Familiarity with Vector Database integration and retrieval validation Background in data science covering ML concepts, data pipelines or data engineering collaboration Proficiency in API tooling such as Postman and Bruno Experience with quality-gate implementation in CI/CD pipelines such as Azure DevOps or Jenkins We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: Lead AI Testing Engineer role focused on defining QA strategy and building automation frameworks for AI-driven systems, knowledge graphs, ontologies and RAG-based LLM features. The position covers end-to-end testing across data ingestion, graph layers, rule engines, backend APIs and frontend applications with integration into CI/CD pipelines. The offer includes remote work flexibility, professional development programs, certifications and comprehensive benefits. 🗂️ Requirements: 8+ years of experience in software development, test automation and DevOps, Python test automation with pytest or equivalent, Experience with AI/ML library integration, Knowledge Graph or Data Ontology testing experience, Expertise in OWL2, Expertise in RDF, Expertise in SPARQL, SHACL validation skills, Experience with GraphDB or similar graph databases, QA strategy definition for complex data-heavy systems, SQL data validation skills, SPARQL and Cypher query language skills, Backend and REST API testing experience, Frontend test automation with Playwright or equivalent, Hands-on experience with coding agents and agentic development, Experience testing RAG-based Gen AI / LLM applications, Knowledge of Gen AI / LLM evaluation frameworks and metrics, Experience automating Gen AI/RAG evaluation at scale, Agile delivery experience, Experience with Git, Experience with Azure DevOps or JIRA, English proficiency at B2 level or higher 📃 Skills: Python, pytest, OWL2, RDF, SPARQL, SHACL, GraphDB, SQL, Cypher, REST, Playwright, Git, Azure, JIRA, Jenkins, Postman, Bruno, LLM, RAG, GenAI 🏢 Description: We are looking for a Lead AI Testing Engineer to drive the quality strategy for AI-driven and data migration initiatives spanning knowledge graphs, ontologies and RAG-based LLM features. You will define and execute QA practices across data ingestion, graph layers, rule evaluation engines and AI/LLM components while building automation frameworks that scale evaluation across complex, data-heavy systems. Responsibilities Design and automate evaluation of RAG-based and LLM-driven features including grounding, answer accuracy, determinism/reproducibility, precision, recall and hallucination rate Build test harnesses to scale evaluation beyond human-in-the-loop processes Create data-driven test suites for underwriting rules and validate rule execution via SPARQL evaluator, arithmetic/threshold logic and multi-condition scenarios Verify ontology schema correctness and instance accuracy against source data, perform reasoner consistency checks and SHACL validation Define and drive end-to-end QA strategy covering data ingestion, ontology/graph layer, rule evaluation engine, AI/LLM layer and integration with underwriting solutions Establish quality gates, acceptance criteria and test coverage models Maintain automation test suites across knowledge graph, data ontology layer, backend services and frontend layers Validate end-to-end flows across ontology, rule engine and decision output, perform contract testing and cross-system data consistency validation Integrate test suites into CI/CD pipelines and define release quality gates Champion automation-first and shift-left quality practices Leverage agentic AI and Gen AI tooling in testing and framework development Requirements 8+ years of experience in software development, testing automation and DevOps Proficiency in Python test automation using pytest or equivalent, scripting and AI/ML library integration Expertise in testing Knowledge Graph or Data Ontology solutions using OWL2, RDF and SPARQL Skills in SHACL validation and graph databases such as GraphDB, including ontology validation, entity/relationship integrity, reasoner consistency checks and graph query correctness Proven ability to define and drive QA strategy independently for complex data-heavy systems, including building automation frameworks and implementing quality gates Skills in data validation using SQL and graph query languages such as SPARQL and Cypher, along with understanding of deterministic vs probabilistic systems Background in backend and API testing (REST) covering data validation, integration and E2E testing Proficiency in frontend web application test automation using Playwright or equivalent Python-compatible framework Hands-on experience using coding agents and agentic development daily Demonstrated experience testing and evaluating RAG-based Gen AI / LLM applications including grounding, answer accuracy and hallucination/determinism checks Applied knowledge of Gen AI / LLM evaluation frameworks and metrics such as precision, recall, criteria recall and efficiency, with proven ability to automate Gen AI/RAG evaluation at scale Experience in Agile delivery using Git and Azure DevOps or JIRA English proficiency at B2 level or higher Nice to have Skills in semantic search testing and evaluation Familiarity with Vector Database integration and retrieval validation Background in data science covering ML concepts, data pipelines or data engineering collaboration Proficiency in API tooling such as Postman and Bruno Experience with quality-gate implementation in CI/CD pipelines such as Azure DevOps or Jenkins We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: Lead AI Testing Engineer role focused on defining and automating QA strategy for AI-driven, RAG-based LLM, knowledge graph, and data ontology systems. The position involves building scalable automation frameworks, validating complex data and rule-engine workflows, and integrating quality processes into CI/CD pipelines. Candidates will work across backend, frontend, graph databases, and AI evaluation frameworks in large-scale data-heavy environments. 🗂️ Requirements: 8+ years of experience in software development, test automation and DevOps, Proficiency in Python test automation, Experience with pytest or equivalent frameworks, Expertise in OWL2, RDF and SPARQL, Experience testing Knowledge Graph or Data Ontology solutions, Skills in SHACL validation, Experience with GraphDB or similar graph databases, Ability to define and drive QA strategy independently, Experience building automation frameworks and quality gates, Skills in SQL, SPARQL and Cypher, Experience with REST API testing, Experience with frontend automation using Playwright or equivalent, Hands-on experience with coding agents and agentic development, Experience testing RAG-based Gen AI and LLM applications, Knowledge of Gen AI and LLM evaluation metrics, Experience automating Gen AI/RAG evaluation at scale, Experience with Git and Azure DevOps or JIRA, English proficiency at B2 level or higher 📃 Skills: Python, pytest, OWL2, RDF, SPARQL, SHACL, GraphDB, SQL, Cypher, REST, Playwright, Git, Azure, JIRA, LLM, RAG, GenAI, CI/CD, Jenkins, Postman, Bruno 🏢 Description: We are looking for a Lead AI Testing Engineer to drive the quality strategy for AI-driven and data migration initiatives spanning knowledge graphs, ontologies and RAG-based LLM features. You will define and execute QA practices across data ingestion, graph layers, rule evaluation engines and AI/LLM components while building automation frameworks that scale evaluation across complex, data-heavy systems. Responsibilities Design and automate evaluation of RAG-based and LLM-driven features including grounding, answer accuracy, determinism/reproducibility, precision, recall and hallucination rate Build test harnesses to scale evaluation beyond human-in-the-loop processes Create data-driven test suites for underwriting rules and validate rule execution via SPARQL evaluator, arithmetic/threshold logic and multi-condition scenarios Verify ontology schema correctness and instance accuracy against source data, perform reasoner consistency checks and SHACL validation Define and drive end-to-end QA strategy covering data ingestion, ontology/graph layer, rule evaluation engine, AI/LLM layer and integration with underwriting solutions Establish quality gates, acceptance criteria and test coverage models Maintain automation test suites across knowledge graph, data ontology layer, backend services and frontend layers Validate end-to-end flows across ontology, rule engine and decision output, perform contract testing and cross-system data consistency validation Integrate test suites into CI/CD pipelines and define release quality gates Champion automation-first and shift-left quality practices Leverage agentic AI and Gen AI tooling in testing and framework development Requirements 8+ years of experience in software development, testing automation and DevOps Proficiency in Python test automation using pytest or equivalent, scripting and AI/ML library integration Expertise in testing Knowledge Graph or Data Ontology solutions using OWL2, RDF and SPARQL Skills in SHACL validation and graph databases such as GraphDB, including ontology validation, entity/relationship integrity, reasoner consistency checks and graph query correctness Proven ability to define and drive QA strategy independently for complex data-heavy systems, including building automation frameworks and implementing quality gates Skills in data validation using SQL and graph query languages such as SPARQL and Cypher, along with understanding of deterministic vs probabilistic systems Background in backend and API testing (REST) covering data validation, integration and E2E testing Proficiency in frontend web application test automation using Playwright or equivalent Python-compatible framework Hands-on experience using coding agents and agentic development daily Demonstrated experience testing and evaluating RAG-based Gen AI / LLM applications including grounding, answer accuracy and hallucination/determinism checks Applied knowledge of Gen AI / LLM evaluation frameworks and metrics such as precision, recall, criteria recall and efficiency, with proven ability to automate Gen AI/RAG evaluation at scale Experience in Agile delivery using Git and Azure DevOps or JIRA English proficiency at B2 level or higher Nice to have Skills in semantic search testing and evaluation Familiarity with Vector Database integration and retrieval validation Background in data science covering ML concepts, data pipelines or data engineering collaboration Proficiency in API tooling such as Postman and Bruno Experience with quality-gate implementation in CI/CD pipelines such as Azure DevOps or Jenkins We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: Lead AI Testing Engineer role focused on defining QA strategy and automation for AI-driven systems, knowledge graphs, ontologies and RAG-based LLM applications. The position covers end-to-end testing across data ingestion, graph layers, rule engines, backend/frontend services and CI/CD pipelines using automation-first practices. Candidates will work on scalable evaluation frameworks for GenAI features, data validation and complex enterprise integrations. 🗂️ Requirements: 8+ years of experience in software development, test automation and DevOps, Proficiency in Python test automation, Experience with pytest or equivalent framework, Expertise in Knowledge Graph or Data Ontology testing, Knowledge of OWL2, Knowledge of RDF, Knowledge of SPARQL, Experience with SHACL validation, Experience with GraphDB or similar graph databases, Ability to define and drive QA strategy independently, Experience building automation frameworks and quality gates, Skills in SQL and graph query languages, Understanding of deterministic and probabilistic systems, Experience in backend and REST API testing, Experience in frontend test automation using Playwright or equivalent, Hands-on experience with coding agents and agentic development, Experience testing RAG-based GenAI and LLM applications, Knowledge of GenAI/LLM evaluation frameworks and metrics, Experience automating GenAI/RAG evaluation at scale, Experience with Agile delivery, Knowledge of Git, Experience with Azure DevOps or JIRA, English proficiency at B2 level or higher 📃 Skills: Python, pytest, OWL2, RDF, SPARQL, SHACL, GraphDB, SQL, Cypher, REST, Playwright, Git, Azure, JIRA, Jenkins, Postman, Bruno, RAG, LLM, GenAI, CI/CD 🏢 Description: We are looking for a Lead AI Testing Engineer to drive the quality strategy for AI-driven and data migration initiatives spanning knowledge graphs, ontologies and RAG-based LLM features. You will define and execute QA practices across data ingestion, graph layers, rule evaluation engines and AI/LLM components while building automation frameworks that scale evaluation across complex, data-heavy systems. Responsibilities Design and automate evaluation of RAG-based and LLM-driven features including grounding, answer accuracy, determinism/reproducibility, precision, recall and hallucination rate Build test harnesses to scale evaluation beyond human-in-the-loop processes Create data-driven test suites for underwriting rules and validate rule execution via SPARQL evaluator, arithmetic/threshold logic and multi-condition scenarios Verify ontology schema correctness and instance accuracy against source data, perform reasoner consistency checks and SHACL validation Define and drive end-to-end QA strategy covering data ingestion, ontology/graph layer, rule evaluation engine, AI/LLM layer and integration with underwriting solutions Establish quality gates, acceptance criteria and test coverage models Maintain automation test suites across knowledge graph, data ontology layer, backend services and frontend layers Validate end-to-end flows across ontology, rule engine and decision output, perform contract testing and cross-system data consistency validation Integrate test suites into CI/CD pipelines and define release quality gates Champion automation-first and shift-left quality practices Leverage agentic AI and Gen AI tooling in testing and framework development Requirements 8+ years of experience in software development, testing automation and DevOps Proficiency in Python test automation using pytest or equivalent, scripting and AI/ML library integration Expertise in testing Knowledge Graph or Data Ontology solutions using OWL2, RDF and SPARQL Skills in SHACL validation and graph databases such as GraphDB, including ontology validation, entity/relationship integrity, reasoner consistency checks and graph query correctness Proven ability to define and drive QA strategy independently for complex data-heavy systems, including building automation frameworks and implementing quality gates Skills in data validation using SQL and graph query languages such as SPARQL and Cypher, along with understanding of deterministic vs probabilistic systems Background in backend and API testing (REST) covering data validation, integration and E2E testing Proficiency in frontend web application test automation using Playwright or equivalent Python-compatible framework Hands-on experience using coding agents and agentic development daily Demonstrated experience testing and evaluating RAG-based Gen AI / LLM applications including grounding, answer accuracy and hallucination/determinism checks Applied knowledge of Gen AI / LLM evaluation frameworks and metrics such as precision, recall, criteria recall and efficiency, with proven ability to automate Gen AI/RAG evaluation at scale Experience in Agile delivery using Git and Azure DevOps or JIRA English proficiency at B2 level or higher Nice to have Skills in semantic search testing and evaluation Familiarity with Vector Database integration and retrieval validation Background in data science covering ML concepts, data pipelines or data engineering collaboration Proficiency in API tooling such as Postman and Bruno Experience with quality-gate implementation in CI/CD pipelines such as Azure DevOps or Jenkins We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: The offer is for a QA Engineer responsible for ensuring the quality and reliability of business applications through manual and automated testing across the full software delivery lifecycle. The role involves creating and maintaining automated tests, integrating them into CI/CD pipelines, and collaborating closely with development teams in an AI-driven environment. It combines hands-on testing, quality risk analysis, and continuous improvement of QA practices. 🗂️ Requirements: Minimum 4 years of experience in software quality assurance, Hands-on experience in manual testing, Hands-on experience in test automation, Programming skills in Python, Java, C#, or similar, Experience with version control systems, Knowledge of test automation frameworks, Experience integrating automated tests with CI pipelines, Understanding of software development practices, Ability to analyze requirements and define test coverage, Experience using AI tools in engineering work 📃 Skills: Python, Java, C#, Git, CI/CD, Automation, Testing, AI 🏢 Description: We are looking for a QA Engineer who will help ensure that business applications are reliable, user-friendly, and ready for production use. In this role, you will work closely with developers, project stakeholders, and end users to understand requirements, identify risks early, and support quality throughout the full software delivery lifecycle. The position combines manual testing, test automation, collaboration with technical teams, and continuous improvement of QA practices. It is well suited to someone who enjoys finding issues, improving processes, and using both technical skills and curiosity to deliver better software. The company operates in the financial sector and has a strong AI-oriented culture. Artificial intelligence is used in a practical way to support daily work, automate repetitive tasks, improve efficiency, and speed up delivery. As part of the recruitment process, the candidate’s AI mindset will also be assessed, including openness to using modern AI tools, ability to learn with AI support, critical review of AI-generated outputs, responsible usage, and readiness to identify areas where AI can improve QA and engineering work. Responsibilities Prepare and maintain test documentation, including test scenarios, test cases, and testing procedures. Perform different types of testing, including functional, regression, integration, UI, data validation, exploratory, and end-to-end testing. Verify applications from both technical and user perspectives to identify defects and usability improvements. Work with business users to clarify requirements and understand expected system behavior. Cooperate with developers, project managers, and other stakeholders during planning, daily meetings, and delivery activities. Identify quality risks early and suggest practical ways to reduce them. Support improvements in testing standards, development quality, and release readiness. Create and maintain automated tests using programming languages such as Python, Java, or C#. Participate in code reviews related to test automation and quality tooling. Help integrate automated tests into CI/CD processes. Share QA knowledge with the team and contribute to internal documentation or user guidance. Requirements At least 4 years of experience in software quality assurance. Hands-on experience with manual testing and test automation. Programming skills in Python, Java, C#, or a similar language. Understanding of software development practices and ability to work closely with engineering teams. Experience with version control tools such as Git or similar systems. Knowledge of test automation frameworks and interest in connecting automated tests with CI pipelines. Ability to analyze requirements, spot risks, and translate them into effective test coverage. Comfortable using AI tools to support learning, testing, automation, and daily engineering tasks. Responsible approach to AI-assisted work, including validation, critical thinking, and awareness of limitations. Strong problem-solving skills and attention to detail. Good communication skills and ability to work with both technical and non-technical stakeholders. Proactive attitude, willingness to learn, and flexibility in taking on different QA-related responsibilities.