Senior AI Engineer – finance industry (f/m/x)

🏢 Summary: Full-time remote Senior AI Engineer role focused on designing and operating production-grade GenAI systems, including agentic workflows and RAG pipelines integrated into enterprise platforms. The position emphasizes backend engineering, API integrations, and orchestration of LLM capabilities in secure, scalable cloud environments rather than ML research. The role also includes mentoring engineers and establishing best practices for reliable, observable AI services. 🗂️ Requirements: 5+ years professional software development experience, Proficiency in Python, Experience building production LLM applications, Hands-on experience with RAG systems, Experience designing and integrating RESTful APIs, Strong understanding of distributed systems fundamentals, Experience with SQL databases and performance optimization, Experience with vector search, Experience integrating external services via APIs, Knowledge of Azure cloud services, Experience with unit and integration testing, Ability to build AI evaluation and testing frameworks 📃 Skills: Python, REST, GraphQL, SQL, Azure, RAG, LLM, LangChain, LangGraph, ASP.NET, APIs, Microservices, DistributedSystems, Concurrency, AsyncIO, VectorSearch, Embeddings, Caching, AzureFunctions, Storage, KeyVault, AppConfiguration, ApplicationInsights, ServiceBus, EventHubs 🏢 Description: Workload: full-time Work model: 100% Remote We are seeking a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience building production GenAI solutions, including agentic workflows and Retrieval-Augmented Generation (RAG). This is an engineering and orchestration role focused on integrating LLM capabilities into enterprise systems – not a traditional model-training/ML research role. 5+ years of professional software development experience (ideally 7+ years across backend/API/integration and cloud platforms). Proven ability to ship production-grade LLM applications ( RAG , tool/function calling, agent orchestration) with reliability, security, and observability. Strong ownership mindset and passion for AI engineering – curiosity, experimentation, and a drive to continuously improve the product and the team. Excellent communication and collaboration skills; ability to guide, mentor, and unblock other engineers as we build out an AI engineering capability. Main Responsibilities: Design, build, and operate agentic AI services that orchestrate tools, workflows, and integrations across cloud systems and enterprise data sources. Implement and continuously improve RAG pipelines for tax artifacts and internal knowledge, including ingestion, retrieval tuning, and evaluation. Integrate AI workflows with existing internal platforms (e.g., assistant frameworks) and back-end services through robust APIs. Define and maintain tool/function schemas and orchestration patterns; implement streaming updates, interrupts, and human-in-the-loop steps as needed. Partner with other engineers to set direction, mentor, and unblock the team — helping establish strong foundations for the AI initiative. Build in quality from day one: automated tests, evaluation checks, monitoring/telemetry, and performance optimization for network-bound workloads. Participate in Agile ceremonies (daily scrums, refinement/grooming, planning) and collaborate through peer review, pair programming, and strong documentation. Apply best practices, design principles, and security standards throughout the SDLC, with a focus on reliability and responsible AI. Key Requirements: Strong software engineering background (not a research-only data science profile): designing, building, and operating production systems. Proficiency in at least one backend language used for AI systems (Python preferred). Hands-on experience building and integrating RESTful APIs ; GraphQL experience is a plus. Strong understanding of distributed systems fundamentals : concurrency, async I/O, resiliency/retries, rate limits, caching, and performance optimization. Experience integrating with external services and internal platforms via APIs and event-driven patterns. Solid database fundamentals (SQL design, performance, migrations); experience with vector search is required, and hybrid search stores are a plus. Hands-on experience building LLM-powered applications end-to-end : prompt design, tool/function interfaces, structured outputs, and streaming user experiences. Experience with RAG systems : document ingestion pipelines, chunking/metadata, embeddings, retrieval strategies, grounding, and evaluation. Cloud services expertise : Strong knowledge of Azure cloud services used for enterprise AI solutions (e.g., Functions, Storage, Key Vault, App Configuration, Application Insights). Development practices experience : Strong background in unit and integration testing; ability to build and maintain AI evaluation harnesses (golden sets, regression tests, automated checks). Nice to Have: Direct experience with LangGraph and/or LangChain for multi-agent workflows. Familiarity with emerging agentic ecosystem concepts/protocols (e.g., MCP, A2A, ADK or similar). Experience integrating AI services into .NET (ASP.NET Core) applications or building AI microservices that serve enterprise applications. Experience with event-driven architectures (service bus, event hubs) and real-time updates/streaming to UI. Experience working with tax/enterprise document corpora and governance constraints (PII, retention, access control). Other Details: This position is designed for remote work and has a flexible duration, allowing for innovation in the AI space within an agile environment.

🏢 Summary: Full-time remote Senior AI Engineer role focused on designing and operating production-grade GenAI solutions, including agentic workflows and RAG pipelines integrated into enterprise systems. The position emphasizes software engineering excellence, cloud-based AI services, and orchestration of LLM capabilities rather than model research. You will build reliable, secure, and observable AI services integrated with internal platforms and APIs. 🗂️ Requirements: 5+ years of professional software development experience, Strong background in designing and operating production systems, Proficiency in Python or another backend language for AI systems, Experience building and integrating RESTful APIs, Understanding of distributed systems fundamentals, Experience with API integrations and event-driven architectures, Solid SQL and database design knowledge, Experience with vector search, Hands-on experience building end-to-end LLM applications, Experience implementing RAG pipelines, Strong knowledge of Azure cloud services, Experience with unit and integration testing, Ability to build AI evaluation and regression testing frameworks 📃 Skills: Python, REST, GraphQL, SQL, Azure, RAG, LLM, LangChain, LangGraph, ASP.NET, APIs, Embeddings, VectorSearch, AzureFunctions, AzureStorage, KeyVault, AppConfiguration, ApplicationInsights 🏢 Description: Workload: full-time Work model: 100% Remote We are seeking a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience building production GenAI solutions, including agentic workflows and Retrieval-Augmented Generation (RAG). This is an engineering and orchestration role focused on integrating LLM capabilities into enterprise systems – not a traditional model-training/ML research role. 5+ years of professional software development experience (ideally 7+ years across backend/API/integration and cloud platforms). Proven ability to ship production-grade LLM applications ( RAG , tool/function calling, agent orchestration) with reliability, security, and observability. Strong ownership mindset and passion for AI engineering – curiosity, experimentation, and a drive to continuously improve the product and the team. Excellent communication and collaboration skills; ability to guide, mentor, and unblock other engineers as we build out an AI engineering capability. Main Responsibilities: Design, build, and operate agentic AI services that orchestrate tools, workflows, and integrations across cloud systems and enterprise data sources. Implement and continuously improve RAG pipelines for tax artifacts and internal knowledge, including ingestion, retrieval tuning, and evaluation. Integrate AI workflows with existing internal platforms (e.g., assistant frameworks) and back-end services through robust APIs. Define and maintain tool/function schemas and orchestration patterns; implement streaming updates, interrupts, and human-in-the-loop steps as needed. Partner with other engineers to set direction, mentor, and unblock the team — helping establish strong foundations for the AI initiative. Build in quality from day one: automated tests, evaluation checks, monitoring/telemetry, and performance optimization for network-bound workloads. Participate in Agile ceremonies (daily scrums, refinement/grooming, planning) and collaborate through peer review, pair programming, and strong documentation. Apply best practices, design principles, and security standards throughout the SDLC, with a focus on reliability and responsible AI. Key Requirements: Strong software engineering background (not a research-only data science profile): designing, building, and operating production systems. Proficiency in at least one backend language used for AI systems (Python preferred). Hands-on experience building and integrating RESTful APIs ; GraphQL experience is a plus. Strong understanding of distributed systems fundamentals : concurrency, async I/O, resiliency/retries, rate limits, caching, and performance optimization. Experience integrating with external services and internal platforms via APIs and event-driven patterns. Solid database fundamentals (SQL design, performance, migrations); experience with vector search is required, and hybrid search stores are a plus. Hands-on experience building LLM-powered applications end-to-end : prompt design, tool/function interfaces, structured outputs, and streaming user experiences. Experience with RAG systems : document ingestion pipelines, chunking/metadata, embeddings, retrieval strategies, grounding, and evaluation. Cloud services expertise : Strong knowledge of Azure cloud services used for enterprise AI solutions (e.g., Functions, Storage, Key Vault, App Configuration, Application Insights). Development practices experience : Strong background in unit and integration testing; ability to build and maintain AI evaluation harnesses (golden sets, regression tests, automated checks). Nice to Have: Direct experience with LangGraph and/or LangChain for multi-agent workflows. Familiarity with emerging agentic ecosystem concepts/protocols (e.g., MCP, A2A, ADK or similar). Experience integrating AI services into .NET (ASP.NET Core) applications or building AI microservices that serve enterprise applications. Experience with event-driven architectures (service bus, event hubs) and real-time updates/streaming to UI. Experience working with tax/enterprise document corpora and governance constraints (PII, retention, access control). Other Details: This position is designed for remote work and has a flexible duration, allowing for innovation in the AI space within an agile environment.

🏢 Summary: Senior-level role focused on designing and delivering scalable AI/ML solutions, including LLMs, RAG, and agent-based systems, from architecture to production deployment. The position involves leading technical decisions, writing high-quality Python code, and building cloud-based microservices solutions. The role also requires translating business needs into robust, production-ready AI systems. 🗂️ Requirements: Several years of software development experience (senior-level), Strong Python proficiency, Solid understanding of ML/AI ecosystem (LLMs, NLP, deep learning), Hands-on experience with RAG, prompt engineering, agent-based systems, Experience with microservices architecture, Experience with databases, Experience with Kubernetes, Experience with CI/CD pipelines, Experience with Azure cloud, English proficiency 📃 Skills: Python, LLMs, RAG, NLP, DeepLearning, PromptEngineering, Microservices, Databases, Kubernetes, CICD, Azure, GCP, Java 🏢 Description: Responsibilities: Design and develop scalable AI/ML solutions (LLMs, RAG, agentic systems) Write high-quality, testable Python code Lead technical architecture and make key design decisions for critical projects Own end-to-end delivery of solutions, from design to production deployment Collaborate with business stakeholders and translate requirements into technical solutions Requirements: Several years of experience as a software developer (senior-level preferred) Very strong Python skills and solid understanding of the ML/AI ecosystem (LLMs, NLP, deep learning) Hands-on experience with RAG, prompt engineering, and agent-based systems Experience with microservices architecture, databases, Kubernetes, and CI/CD pipelines Experience with Azure cloud (GCP as a plus) Basic familiarity with Java is a plus Strong English communication skills

🏢 Summary: Senior-level role focused on building AI-driven backend applications using Python, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). The position involves developing scalable APIs, integrating retrieval pipelines, and deploying services in cloud environments. The role combines advanced AI implementation with solid backend engineering practices. 🗂️ Requirements: 5+ years commercial experience in Python development, Strong backend engineering experience (APIs, services, integrations), Hands-on experience with LLMs, Hands-on experience with RAG implementations, Experience with Azure, AWS, or GCP, Knowledge of software development best practices 📃 Skills: Python, LLMs, RAG, Azure, AWS, GCP, APIs 🏢 Description: Senior Python AI Developer We are looking for an experienced Python Developer to join a team building modern AI-driven applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) , alongside standard backend development work. Key Responsibilities Develop and maintain backend services and APIs in Python Build AI-enabled application features using LLMs and RAG workflows Design and integrate retrieval pipelines for contextual AI responses Work with databases, external APIs, and internal systems Deploy and operate services in Azure or other cloud environments Write clean, testable, and scalable production code Requirements 5+ years of commercial experience in Python development Strong background in backend engineering (APIs, services, integrations) Hands-on experience with LLMs and RAG implementations Cloud experience with Azure , AWS, or GCP Solid understanding of software development best practices Tech Stack Python LLMs, RAG Azure (or other cloud platforms)

🏢 Summary: The offer is for a backend-focused AI Engineer role building a modern AI platform for business process automation and multi-agent system integration in a production-grade enterprise environment. The position centers on developing scalable Python-based services, secure API integrations, and cloud-native AI solutions using LLMs and RAG. The role involves designing robust, secure, and scalable architectures deployed on AWS or Azure with containerized infrastructure. 🗂️ Requirements: Extensive experience in backend development with Python in production environments, Hands-on experience with AI Agents, RAG, MCP, or LLM-based solutions, Experience with system integrations and API architecture, Experience with scalable cloud-based applications, Experience with AWS or Azure, Experience with Docker and Kubernetes, Experience designing secure and scalable enterprise solutions, Experience with multi-agent systems and workflow automation, Ability to conduct code reviews and mentor developers, Fluent English and Polish 📃 Skills: Python, LLM, RAG, MCP, AWS, Azure, Docker, Kubernetes, API, Microservices, Cloud, AI, Agents 🏢 Description: Additional information The project focuses on building and developing a modern AI platform supporting business process automation and the integration of intelligent agents with enterprise systems. The team is creating scalable solutions based on Python, cloud technologies, and multi-agent architecture, leveraging technologies such as LLMs, RAG, and advanced workflow automation. A key aspect of the project is designing secure integrations between AI platforms, internal systems, and external APIs within a production-grade environment. The project is delivered in an international technology-driven environment with a strong focus on innovation, high-quality architecture, and the development of cutting-edge AI solutions for a global industrial organization. You’re ideal for this role if you: Have extensive experience in backend development with Python in production environments Have hands-on experience with AI technologies such as AI Agents, RAG, MCP, or LLM-based solutions Understand system integrations, API architecture, and scalable cloud-based applications Have worked with AWS or Azure and containerized environments such as Docker and Kubernetes Are experienced in designing secure and scalable enterprise solutions Have strong problem-solving skills and can proactively remove technical blockers Enjoy working closely with cross-functional teams in an international environment Have experience mentoring developers, conducting code reviews, and promoting engineering best practices Are interested in modern AI architectures, workflow automation, and multi-agent systems Communicate fluently in English and Polish and feel comfortable working in a global setup Your day-to-day responsibilities include:​ Designing and developing scalable backend services and AI-driven integration solutions in Python Building and maintaining APIs, connectors, and middleware between AI platforms and enterprise systems Creating and optimizing multi-agent workflows and intelligent automation processes Collaborating with AI Engineers, Data Engineers, and other technical stakeholders on system architecture and integrations Integrating external AI platforms, internal tools, and cloud services into production environments Working with cloud technologies and containerized infrastructure in AWS or Azure environments Improving the quality, reliability, and performance of AI-powered solutions and LLM-based workflows Conducting code reviews, mentoring team members, and supporting engineering best practices Troubleshooting technical issues, identifying bottlenecks, and driving continuous improvements Contributing to technical documentation, architecture discussions, and innovation initiatives across the team

🏢 Summary: Lead AI Software Developer role focused on architecting and scaling AI-powered applications, backend systems, and multi-agent workflows across cloud and enterprise environments. The position involves designing secure integrations, optimizing LLM-based solutions, and leading technical delivery and best practices. It combines hands-on AI development with backend architecture and technical leadership. 🗂️ Requirements: Degree in Computer Science, AI, Data Science or related field, 8+ years of software engineering experience, 5+ years of Python backend development in production, Experience building and scaling Python applications, Hands-on experience with AI Agents, RAG and MCP, Experience with AWS or Azure cloud platforms, Experience with storage systems and databases, Knowledge of containerization and Kubernetes, Experience with API integrations and enterprise system connectivity, Leadership and mentoring experience 📃 Skills: Python, AWS, Azure, Kubernetes, RAG, MCP, APIs, Cloud, Databases, Containerization, AI, LLM 🏢 Description: We are seeking an experienced Lead AI Software Developer to design and scale AI-powered applications and integrations. In this role, you will architect backend systems, lead AI agent development, and ensure secure, scalable integration across cloud platforms and enterprise systems. Key Responsibilities Design and maintain middleware, APIs, and connectors integrating AI agents with enterprise systems Architect scalable backend services and cloud-based solutions (AWS/Azure) Develop and optimize multi-agent workflows and adaptive decision logic Fine-tune prompts and LLM-based solutions for domain-specific use cases Lead code reviews, mentor developers, and define development best practices Drive complex API integrations with internal and external AI platforms (incl. MCP servers) Remove technical roadblocks and ensure timely delivery Collaborate with AI, Data, and Platform teams to optimize automation architectures Maintain technical documentation and promote continuous improvement Requirements Degree in Computer Science, AI, Data Science, or similar field 8+ years of software engineering experience, including 5+ years of Python backend development (production) Strong experience building and scaling Python applications Hands-on experience with AI technologies (AI Agents, RAG, MCP) Experience with cloud platforms (AWS or Azure), storage, databases, and AI services Knowledge of containerization and Kubernetes Experience with API integrations and enterprise system connectivity Strong leadership and mentoring experience Excellent communication skills; fluent in Polish and English (min. B2); German is a plus Willingness to travel internationally (~10%) Nice to have: Experience with LangChain, LangGraph, multi-agent systems, prompt engineering, and LLM fine-tuning. What We Offer Work in a global and innovative technology environment Direct impact on enterprise AI and automation solutions Professional development and access to training programs Hybrid work model with flexible hours (central Wroclaw office) Competitive benefits package, cafeteria system, CSR initiatives, and employee clubs

🏢 Summary: Opportunity for a Senior AI Solutions Architect to design and lead production-grade LLM systems focused on retrieval-augmented generation and knowledge graph integration. The role involves building end-to-end orchestration layers, semantic search pipelines, and evaluation frameworks to deliver reliable, context-aware AI solutions. You will work with vector and graph databases, advanced embedding strategies, and enterprise AI platforms in a scalable cloud environment. 🗂️ Requirements: Experience designing and implementing LLM-based systems in production, Hands-on experience with retrieval-augmented generation (RAG), Strong knowledge of embeddings, vector search, and chunking strategies, Experience building entity extraction pipelines, Experience working with knowledge graphs, Proficiency in Python, Understanding of prompt engineering, Experience designing agent workflows, Experience defining evaluation frameworks and quality metrics for AI systems, Familiarity with distributed systems and scalable data architectures, Experience implementing observability, logging, and tracing 📃 Skills: Python, SQL, RAG, LLM, Embeddings, VectorDB, GraphDB, GCP, CloudSpanner, VertexAI, Gemini, IAM, Encryption, Observability 🏢 Description: Project overview This project focuses on building a scalable AI platform that transforms expert knowledge into structured graph based intelligence and connects it with enterprise grade language models. The solution emphasizes semantic search, agent workflows, and robust evaluation frameworks to ensure reliable outputs. Position overview We are looking for a Senior AI Solutions Architect to lead the design and implementation of advanced LLM driven solutions with a strong focus on retrieval augmented generation and knowledge graph integration. You will take ownership of the full orchestration layer, shaping how structured and unstructured data is transformed into high quality, context aware AI responses. Technology stack Python, SQL, vector databases, graph databases, Google Cloud Platform, Cloud Spanner, Vertex AI, Gemini, LLM frameworks, embedding models, observability tools, IAM, encryption Responsibilities Design and manage end to end LLM orchestration and retrieval pipelines Define embedding model selection and chunking strategies, including context window management and trade offs affecting retrieval quality and cost Own the entity extraction pipeline to convert unstructured content into graph nodes and relationships Implement entity resolution, relationship normalization, and deduplication processes Design and refine semantic search strategies and retrieval logic across graph and vector layers Develop prompt engineering approaches and agentic workflows for advanced use cases Integrate graph based outputs with enterprise AI platforms such as Gemini Design and maintain evaluation frameworks including ground truth dataset creation Measure and improve retrieval quality using metrics such as recall, precision at K, faithfulness, and answer relevance Establish systematic regression testing practices for AI pipelines Optimize LLM usage costs across the full retrieval and generation lifecycle Implement observability, logging, and tracing to monitor performance and reliability Requirements Experience designing and implementing LLM based systems in production environments Hands on experience with retrieval augmented generation and semantic search Strong understanding of embeddings, vector search, and chunking strategies Experience building entity extraction pipelines and working with knowledge graphs Proficiency in Python and data processing workflows Understanding of prompt engineering and agent workflow design Experience defining evaluation frameworks and quality metrics for AI systems Familiarity with distributed systems and scalable data architectures Experience implementing observability, logging, and tracing in data intensive environments Nice to have Experience with Google Cloud Platform services including Cloud Spanner and Vertex AI Familiarity with enterprise AI platforms such as Gemini Knowledge of cost optimization techniques for large scale LLM systems Experience with graph data models and hybrid architectures combining graph, relational, and vector data Exposure to advanced evaluation techniques for generative AI and ranking systems What We Offer: Vacation days : Up to 26 business days per year. 10 illness/special days off per year (fully paid, no medical papers needed) for all contract types Health and life insurance (Luxmed) MyBenefit platform with Multisport option Internal psychological support service English language classes from the first working day Access to external learning platforms : O’Reilly, LinkedIn Learning, Udemy, and a wide catalog of diverse internal training Flexible workplace : work from the office, from home, or choose a hybrid option Tech Skills Mentoring Program Opportunities to develop as a public speaker, mentor, or technical interviewer Fully paid idle (bench) when not involved in a project Certification reimbursement (AWS, GCP, Microsoft, etc.)

🏢 Summary: The role focuses on engineering production-grade AI features such as semantic search, intelligent assistants, and automated risk scoring by integrating external AI providers into a cybersecurity platform. You will own the full lifecycle from prototype to reliable, monitored production services, primarily using Python on Azure. This position emphasizes robust software engineering practices over model research or training. 🗂️ Requirements: 5+ years of professional experience building production systems in Python, Expert-level Python programming skills, Experience with ML frameworks and libraries, Proven experience shipping and operating production software, Experience integrating external APIs in production environments, Strong understanding of software architecture and design patterns, Practical experience with CI/CD pipelines, Experience with containerization using Docker, Understanding of AI/ML concepts including LLMs, embeddings, prompt engineering, RAG, Experience with unit and integration testing and mocking external services, Polish work permit, Professional proficiency in English 📃 Skills: Python, Azure, OpenAI, Anthropic, Docker, CI/CD, FastAPI, Flask, Pinecone, Weaviate, Chroma, .NET, REST, LLMs, RAG, Embeddings, Git 🏢 Description: About the Role As a Senior AI/ML Engineer you will be building AI-powered features at the core of our platform - semantic search, an intelligent assistant, automated risk scoring, and much more. We mainly integrate with external AI model providers (OpenAI, Anthropic, and others) to deliver these capabilities to our clients and internal users. This is a software engineering role, not a data science or ML research position. You won't train models from scratch or publish papers. You'll engineer production-grade systems that leverage AI to solve real cybersecurity problems. If you're a strong Python engineer excited about making AI reliable, maintainable, and impactful — we want to talk to you. We're looking for a versatile software engineer to own the journey from AI prototype to reliable production system: building robust integrations with external AI providers or fine-tuned models, standardizing our Python codebase and release pipelines, and ensuring our AI features meet the same quality bar as any other production service. What You'll Do Ship AI features end to end - from integration design through deployment to monitoring in production. You'll build and maintain Python services that power our AI-driven capabilities on Azure. Build production-ready AI / ML integrations - including retry logic, rate limiting, cost monitoring, fallback strategies, and abstraction layers that allow us to swap between model providers. Turn proofs of concept into reliable software - collaborate with our AI experts (who bring deep ML/data science expertise) to take working prototypes and engineer them into tested, documented, production-grade services. Standardize Python engineering practices - establish and evolve our code organization, testing strategies, CI/CD pipelines, and release processes for AI/ML features. Design clean service boundaries - our platform's core runs on .NET. You'll design and maintain well-defined APIs between the Python/AI layer and the rest of the system. Mentor and share knowledge - help other engineers grow their Python and AI engineering skills, and bring engineering rigor to the broader team's practices. Contribute to architecture decisions - work closely with other teams to shape how AI capabilities evolve within the platform. Who You Are Must-Haves 🔹 5+ years of professional experience building and maintaining production systems in Python. 🔹 Expert-level programming skills and proficiency with common ML frameworks and libraries. 🔹 A track record of shipping and operating software - not just building prototypes. You care about testing, monitoring, logging, and what happens after deployment. 🔹 Experience integrating external APIs in production systems - ideally AI/LLM provider APIs (OpenAI, Anthropic, Azure AI, or similar), but strong API integration experience in any domain counts. 🔹 Solid understanding of software architecture and design patterns - you have opinions about how to structure a Python project and can articulate trade-offs. 🔹 Practical knowledge of CI/CD pipelines, containerization (Docker), and modern development workflows. 🔹 A working understanding of AI/ML concepts - you know what embeddings, LLMs, prompt engineering, and RAG are. You can have a productive technical conversation with an ML specialist without needing every concept explained from scratch. 🔹 Experience with testing strategies - unit tests, integration tests, mocking external services. You instinctively write tests, not as an afterthought. 🔹 A highly adaptable, problem-solving and result-oriented mindset with the ability to work independently and take ownership in a fast-paced and dynamic environment. 🔹 Strong communication skills in English (our working language). 🔹 Willingness to learn continuously and share what you learn with the team. Nice-to-Haves 🔸 Experience with Azure cloud services. 🔸 Familiarity with FastAPI, Flask, or similar Python web frameworks. 🔸 Exposure to MLOps tooling or practices (model versioning, experiment tracking, feature stores). 🔸 Hands-on experience in Document AI / Intelligent Document Processing using traditional models and Generative AI. 🔸 Experience with vector databases (Pinecone, Weaviate, Chroma, or similar). 🔸 Any experience with .NET - helpful for cross-team collaboration, but not expected. 🔸 Background in or interest in cybersecurity as a domain. Your Team You'll join our Synapse team - a small, focused group responsible for all AI/ML capabilities within our platform. The team consists of: -> Team Leader / Architect - provides high-level architectural guidance, planning, and cross-stack delivery. -> AI / ML Engineer - deep expertise in AI/ML and data science. Your counterbalance: they bring the ML knowledge, you bring the production engineering discipline. -> AI / Backend Engineer - transitioning from .NET into AI engineering. A teammate you'll mentor and grow alongside. -> You - the person who bridges the gap between AI prototypes and reliable production systems. Why Join Us? / What We Offer: Competitive salary and benefits package. Remote work options and flexible working hours. Actual impact on the choice and shape of solutions developed. Opportunities for professional growth and development. Training and conference budget. A collaborative, innovative work environment with an iterative agile approach. The role is for six months with the possibility of extension. Both full-time and part-time cooperation are possible.

🏢 Summary: The role involves designing and building GenAI-powered services in healthcare using Python, focusing on creating RAG-based LLM agents and deploying scalable solutions on AWS. The engineer will develop, deploy and maintain AI agents with proper monitoring, guardrails and data compliance. The position requires strong full-stack and AI expertise to deliver reliable, production-ready intelligent systems. 🗂️ Requirements: 5+ years of Python or ReactJS development experience, Recent hands-on experience with LLM or GenAI projects, Hands-on expertise with LangChain, LangGraph or similar frameworks, Understanding of agentic design patterns (tools, planners, memory), Knowledge of vector databases and embeddings, Experience with prompt engineering, Proficiency with Docker, Experience with Git-based CI/CD, Experience with automated testing, Ability to deploy solutions on AWS 📃 Skills: Python, ReactJS, LangChain, LangGraph, LLM, GenAI, RAG, AWS, Docker, Git, CI/CD, Testing, Embeddings, VectorDB, Prompting 🏢 Description: We are seeking a Senior Python AI Engineer (Full Stack) to design and build intelligent GenAI-powered services that solve real business challenges in the healthcare space. In this role, you will develop Python services that power GenAI agents, create retrieval-augmented generation flows, and deploy scalable solutions on AWS while collaborating with cross-functional teams to deliver reliable and safe AI experiences. Responsibilities Build Python services that power the GenAI agent, using LangChain, LangGraph or similar libraries Create retrieval-augmented generation (RAG) flows that pull the right context and streamline LLM responses Run the solution on AWS using containers, serverless or managed AI services Instrument logging, monitoring and guardrails to keep the agent reliable and safe Ensure the ingestion and use of RFP/RFI data follows healthcare data policies Collaborate with product, data science and business teams to make sure the agent solves real problems Document code, APIs and deployment steps so others can maintain and extend the solution Contribute to planning, demos and retros within an Agile team Requirements 5+ years of experience in Python or ReactJS development, with recent focus on LLM or GenAI projects Hands-on expertise in LangChain, LangGraph or comparable agent frameworks Understanding of agentic design patterns and how to wire tools, planners and memory together Knowledge of vector databases, embeddings and prompt engineering Proficiency in Docker, Git-based CI/CD and automated testing Familiarity with AI-assisted development tools (e.g., Cursor, GitHub Copilot, Code Interpreter) to accelerate prototyping and code reviews Solid communication skills and the ability to work with technical and non-technical stakeholders Nice to have Familiarity with healthcare payer workflows, especially RFP/RFI processes Background in integrating with CRMs/workflow tools like Salesforce or ServiceNow Exposure to responsible AI, human-in-the-loop review, or safety tooling AWS or Python certifications We offer/Benefits 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 role involves architecting and deploying advanced AI-driven applications using Azure AI services and modern generative AI frameworks. The position focuses on building scalable knowledge retrieval systems, intelligent AI agents, and integrating AI capabilities into existing products. It requires strong expertise in cloud-native, containerized, and MLOps-driven environments. 🗂️ Requirements: Practical experience with Azure AI ecosystem, Experience with GitHub and DevOps methodologies, Strong programming skills in Python, Hands-on experience with embeddings and text chunking, Experience with vector search solutions, Experience with FastAPI and Pydantic, Experience with pymongo and Azure SDKs, Experience designing scalable AI systems, Experience building document processing pipelines, Experience with cloud-native infrastructures 📃 Skills: Azure, AzureOpenAI, AzureDocumentIntelligence, OCR, LangChain, RAG, CAG, MongoDB, PostgreSQL, Python, FastAPI, Pydantic, pymongo, Docker, GitHub, DevOps, MLOps, Embeddings, VectorSearch, JSON 🏢 Description: We are looking for a skilled AI Full-Stack Developer to architect, implement, and deploy advanced AI-driven applications. The ideal candidate will work with Azure AI services, MongoDB Atlas, LangChain-based workflows, RAG/CAG frameworks, and containerized environments (Docker), while leveraging modern AI agent technologies. Key Responsibilities Design and implement Generative AI solutions using Azure OpenAI, Azure Document Intelligence, OCR capabilities, and other Azure cloud services. Work closely with front-end and back-end teams to seamlessly embed AI functionalities into existing systems. Review and maintain high-quality code for AI service integrations, ensuring best practices and performance standards are met. Create scalable knowledge retrieval systems using LangChain, RAG/CAG approaches, and vector databases—primarily MongoDB Atlas—with working knowledge of PostgreSQL. Develop intelligent AI agents capable of autonomous reasoning, planning, and interaction with external tools. Support and promote AI-augmented development practices with tools such as GitHub Copilot or Factory AI. Requirements Practical experience with the Azure AI ecosystem, GitHub, DevOps methodologies, generative AI architectures, and vector search solutions. Strong programming skills in Python and familiarity with modern MLOps and DevOps environments. Hands-on experience with embeddings and text chunking strategies, JSON and markup formats, FastAPI, Pydantic, pymongo, and Azure SDKs. Proven expertise in designing scalable AI systems, document processing pipelines, and cloud-native infrastructures. Strong communication abilities and experience collaborating within cross-functional teams.