Senior ML Engineer

🏢 Summary: Technical role supporting an AI platform that automates and scales ML processes, focused on maintaining and developing documentation infrastructure and API docs generation. The position involves working with OpenAPI specifications, static site generators, and CI/CD pipelines to ensure high-quality, automated documentation delivery. Includes participation in on-call rotations and collaboration within modern DevOps-driven environments. 🗂️ Requirements: Proven experience with MkDocs and MkDocs-Material, Proficiency in Python, Experience with static site generators, Experience generating API documentation from OpenAPI spec, Strong knowledge of REST API principles, Experience with Git and GitHub workflows, Experience with CI/CD pipelines and GitHub Actions, Scripting skills for task automation, Understanding of dependency management tools (e.g., Poetry), Availability for on-call duty (5 AM–5 PM CEST, once every six weeks) 📃 Skills: Python, MkDocs, Material, OpenAPI, REST, Swagger, Redoc, Git, GitHub, CI/CD, GitHubActions, Poetry, Markdown, Docker, Bash, Shell, HTML, CSS, JavaScript, WCAG 🏢 Description: We’re Devopsbay - MLOPS, DevOps and AI Specialists. We know how nodes works, how to make the cloud cheaper or adapt AI to boost any area that companies need (any many more). We support our clients with strong engineers on a project basis and are always on the lookout for stellar performers. Our clients are at the cutting edge of modern solutions. We also develop our inhouse products: https://descrb.com/ & https://defencebay.com/ Currently, we’re working with a client specialising in AI solutions at scale. The platform helps automate and scale ML processes, allowing access regardless of technical prowess. Please note that working on this project requires availability for on-call duty once every six weeks from 5 AM to 5 PM (CEST time zone). Responsibilities Support projects and smaller “paper cuts” for the docs portal Work with OpenAPI spec for REST services (portions of documentation generated from spec) Markdown, MkDocs/Material CI/CD, Git, basic shell/bash scripting Docker, k9s (work with test/deploy pipelines) Some frontend/CSS experience is a plus Required Skills and Experience Proven experience with MkDocs and MkDocs-Material. Experience with other static site generators (e.g., Hugo, Jekyll, Sphinx) is a plus. Proficiency in Python. Familiarity with dependency management tools like Poetry. Experience with scripting to automate tasks, such as managing redirects, generating documentation, and integrating with external systems. Deep understanding of REST API principles and the OpenAPI Specification. Experience with generating API documentation from spec files (e.g., using tools like Swagger or Redoc). Expertise in using Git and GitHub for version control, including pull request workflows. Experience with continuous integration/continuous deployment (CI/CD) pipelines, particularly with GitHub Actions for tasks like linting (Vale) and build processes. Knowledge of modern development best practices, including writing clean, maintainable code, and testing. Experience with web accessibility standards (e.g., WCAG) is a plus, as it's often a component of a quality front-end experience. Desirable Experience (Plus) Strong proficiency in front-end technologies: HTML, CSS, and JavaScript. Proven capability to translate design specifications into precise, pixel-accurate implementations. Understanding of basic security best practices in a development environment—especially when handling credentials, API keys, and sensitive configuration files. Awareness of potential risks in integrating AI tools (e.g., prompt injection, data exposure) is beneficial. Benefits: B2B contract International projects, often at the cutting edge of technology Experienced team - exchange your expertise with other passionate engineers Modern tech stacks Flexible hours Integration meetings

🏢 Summary: Development of LLM-powered applications and data pipelines for a modern insurance platform, integrating multiple data sources in AWS and Snowflake environments. The role focuses on building and improving a domain-specific chatbot using advanced LLM techniques such as RAG, prompt engineering, and feedback loops. It requires close collaboration with cross-functional teams and availability in the PT timezone. 🗂️ Requirements: Commercial experience with TypeScript, Experience building LLM-powered applications, Hands-on experience with AWS, Experience with Snowflake, Experience designing ETL or ELT pipelines, Experience integrating APIs and external data sources, Ability to design data transformations and data-access layers, Fluent English, Availability for meetings in PT timezone 📃 Skills: TypeScript, Snowflake, AWS, LLM, RAG, APIs, ETL, ELT, Chatbots, SQL, Prompting, Evaluation, Orchestration, EventDriven 🏢 Description: We’re Devopsbay - MLOPS, DevOps and AI Specialists. We know how nodes works, how to make the cloud cheaper or adapt AI to boost any area that companies need (any many more). We support our clients with strong engineers on a project basis and are always on the lookout for stellar performers. Our clients are at the cutting edge of modern solutions. We also develop our inhouse products: https://descrb.com/ & https://defencebay.com/ Please note that working within this project means availability for meetings in PT timezone (mainly till 19:00, usually no later than 18:00) and fluent English. About project: It is a modern insurance brokerage and advisory firm that supports fast-growing companies, particularly in the technology, life sciences, and professional services sectors. The organization combines deep risk management expertise with advanced technology and AI-powered tools to deliver more efficient, tailored insurance solutions to its clients. Its mission is to transform the traditional insurance industry, which is often perceived as complex, slow, and not well suited to the needs of modern businesses. Through digital workflows, automation, and a customer-centric approach, the company simplifies insurance processes, accelerates decision-making, and improves transparency across the entire client experience. By partnering with ambitious leaders and innovative organizations, it helps businesses identify, manage, and mitigate risks associated with growth. Beyond providing insurance coverage, the company offers strategic advisory services that enable clients to make confident decisions and scale their businesses with greater security and peace of mind. What you will do : Work with the following technologies and environments: Agentic AI , TypeScript, Snowflake, AWS, LLM-related technologies , data integrations, APIs, ETL/ELT pipelines. Build LLM-powered applications using modern approaches such as retrieval-augmented generation, tool usage, context management, prompt engineering, evaluation, and feedback loops. Work on a chatbot that can learn from user interactions, improve over time, and support users with domain-specific knowledge. Design and implement integrations with multiple internal and external data sources. Prepare reliable data pipelines, transformations, and data-access layers that enable LLM applications to use structured and unstructured data effectively Take ownership of ambiguous problems and drive solutions end-to-end. Communicate with the team, discuss new ideas, and work closely with non-technical users. Work on a dynamic, startup-style project where learning the domain, experimenting, and iterating quickly are important. Nice To Have: Experience with event-driven architectures and workflow orchestration tools. Experience in regulated domains such as insurance, fintech, or healthcare. Familiarity with AWS services and distributed backend infrastructure. Benefits: B2B contract International projects, often at the cutting edge of technology Experienced team - exchange your expertise with other passionate engineers A challenging yet thriving work environment - we push ourselves to be better everyday Modern tech stacks Remote working possibilities Flexible hours

🏢 Summary: Backend Engineer role focused on building and scaling a mission-critical situational awareness platform with real-time communication and AI-driven capabilities. The position involves developing backend services, APIs, microservices, and data pipelines in a distributed, cloud-based environment. You will integrate AI technologies and external systems while contributing to system architecture and performance optimization. 🗂️ Requirements: 5+ years backend development experience, Strong proficiency in Python, Experience building scalable backend services, Solid knowledge of PostgreSQL and database design, Experience with RabbitMQ or event-driven architectures, Experience with Kubernetes, Experience with AWS, Experience designing RESTful APIs, Experience with microservices architecture, Understanding of distributed systems, Experience with asynchronous communication, Experience with real-time data processing, Experience integrating LLMs or RAG, Experience with data pipelines and ETL, Experience integrating third-party APIs, Knowledge of software architecture and system design, Ability to troubleshoot and optimize production systems 📃 Skills: Python, PostgreSQL, RabbitMQ, Kubernetes, AWS, REST, Microservices, DistributedSystems, LLM, RAG, ETL, APIs, Cloud 🏢 Description: About project: TROP is an advanced situational awareness and operational coordination platform developed by Defencebay. By combining real-time mapping, secure communications, drone integration, AI-ready workflows and resilient Mesh LoRa networking, TROP helps teams maintain a shared operational picture when standard infrastructure is limited or unavailable. We are building next-generation technology for public safety, emergency response, critical infrastructure protection and defense operations. Defencebay is a sub-brand of Devopsbay, focused on building advanced technologies for public safety, defense, and crisis communication systems. The recruitment process for this role is conducted under the Defencebay brand, as part of our dedicated product team working on the TROP platform. While being part of the broader Devopsbay organization, Defencebay operates as an independent product unit responsible for developing mission-critical solutions in the area of situational awareness and resilient communications Required Skills 5+ years of experience in backend software development. Strong proficiency in Python and experience building scalable backend services. Strong understanding of software engineering best practices, including testing, code reviews, clean code, and maintainable architecture. Solid knowledge of PostgreSQL and database design principles. Hands-on experience with message brokers such as RabbitMQ or similar event-driven architectures. Experience working with containerized environments and orchestration platforms, particularly Kubernetes (K8s). Practical knowledge of cloud platforms, preferably AWS. Experience designing, building, and maintaining RESTful APIs and microservices. Understanding of distributed systems, asynchronous communication, and real-time data processing. Experience integrating AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI-powered applications. Familiarity with data pipelines, ETL processes, and analytics workflows. Experience integrating third-party systems, APIs, and external services. Understanding of software architecture principles, system design, and scalability considerations. Ability to troubleshoot complex production issues and optimize system performance. Strong problem-solving skills and ability to work independently in a fast-paced environment. Good communication skills and ability to collaborate effectively within cross-functional teams. Nice to Have Experience working with multiple programming languages and technology stacks beyond Python. Experience with Computer Vision solutions and AI model deployment. Familiarity with Model Context Protocol (MCP) and AI agent architectures. Experience working with Team Awareness Kit (TAK), ATAK, iTAK, or situational awareness platforms. Knowledge of geospatial systems, mapping technologies, or GIS solutions. Experience in defense, public safety, emergency management, or mission-critical systems. Familiarity with DevOps practices, Infrastructure as Code (Terraform), and CI/CD pipelines. What You’ll Do Design, develop, and maintain backend services using technologies such as Python, PostgreSQL, RabbitMQ, Kubernetes (K8s), and AWS . Build and evolve APIs and microservices , with a strong focus on real-time communication features. Develop new features, maintain existing components, and troubleshoot production issues across the platform. Design and implement integrations leveraging modern AI technologies, including LLMs, RAG (Retrieval-Augmented Generation), Computer Vision, and Model Context Protocol (MCP) . Build and maintain data processing pipelines that power AI-driven features and analytics workflows. Evaluate, prototype, and integrate emerging AI technologies into real-world products and solutions. Contribute to system architecture and participate in key technical decisions shaping the platform’s evolution. Integrate with external systems and develop solutions that enable efficient communication and data exchange across distributed services. Work on products within the Team Awareness Kit (TAK) ecosystem , supporting real-time situational awareness, information sharing, and operational coordination. Take ownership of features and projects from initial concept through production deployment and ongoing operation.

🏢 Summary: Remote AI Lead DevOps Engineer role responsible for defining and executing the MLOps and CI/CD strategy for enterprise AI platforms. The position focuses on architecting secure, compliant, and fully automated ML lifecycle governance, ensuring auditability, reproducibility, and large-scale reliability. The role combines technical leadership with hands-on design of cloud-native, DevSecOps-driven AI infrastructure. 🗂️ Requirements: 8–10 years DevOps or Cloud Engineering experience, Minimum 3 years in technical leadership or architect role, Strong knowledge of end-to-end ML lifecycle, Expertise in CI/CD pipeline design and implementation, Advanced Infrastructure as Code experience, Experience with SAST and DAST implementation, Strong IAM and access control management in cloud, Ability to design observability frameworks for ML systems, Experience with configuration management in multi-cloud environments, Knowledge of database scaling and security, Experience implementing model governance and auditability practices 📃 Skills: MLOps, CI/CD, DevSecOps, Azure, AzureDevOps, GitHubActions, Jenkins, Terraform, CloudFormation, SAST, DAST, IAM, Ansible, Puppet, MySQL, PostgreSQL, MongoDB, Observability, Git 🏢 Description: This is a remote position. We are looking for an AI Lead DevOps Engineer to spearhead the MLOps strategy for our high-impact AI accounts. With 8–10 years of experience, you will provide the technical leadership necessary to design robust, compliant, and highly automated AI platforms. You aren't just managing pipelines; you are architect the entire lifecycle governance—ensuring reproducibility, audibility, and security at an enterprise scale. Key Responsibilities: Strategic Leadership: Provide technical direction for the DevOps squad, defining the CI/CD and MLOps roadmap for the account. Model Governance & Evaluation: Implement automated model evaluation pipelines to track accuracy, precision, and recall metrics in production. Enterprise Security: Lead the DevSecOps strategy, ensuring all AI deployments comply with enterprise security standards and global data regulations. Platform Enablement: Architect self-service platforms that allow ML engineers to deploy models with minimal friction while maintaining strict governance guardrails. Auditability & Reproducibility: Ensure that every ML experiment is fully auditable through sophisticated pipeline and dataset versioning strategies. Mentorship: Mentor senior and junior engineers, driving best practices in automation, IaC, and cloud-native architecture. Requirements 8–10 years of experience in DevOps/Cloud Engineering, with at least 3 years in a technical leadership or architect-level role. Deep understanding of the end-to-end ML lifecycle (training, validation, deployment, and retraining loops). Mastery across Azure DevOps, GitHub Actions, and Jenkins. Expert-level Terraform or CloudFormation skills, including modular architecture and cross-account cloud deployments. Significant experience implementing SAST/DAST tools and managing complex IAM/Access Control frameworks in a cloud environment. Ability to design custom observability frameworks that track model drift, pipeline failures, and infrastructure ROI. Advanced knowledge of configuration management tools like Ansible or Puppet for complex multi-cloud environments. Solid understanding of database scaling and security for MySQL, PostgreSQL, and MongoDB. Understanding of how DevOps practices support responsible AI (e.g., bias tracking and audit logs). Exceptional ability to collaborate with Architects and Data Scientists to translate high-level AI needs into operational reality. Native or C1-level English, with the ability to present technical strategies to senior stakeholders. Benefits Professional training programs Work with a team that’s recognized for its excellence. We’ve been featured in the Deloitte Technology Fast 50 & FT 1000 rankings. We’ve also received the Great Place To Work® certification for five years in a row

🏢 Summary: Role focused on designing, building, and deploying AI/ML solutions end-to-end, from data exploration and experimentation to production deployment and MLOps. The position combines Data Science, ML Engineering, and collaboration with business stakeholders to translate data into actionable solutions. You will develop and maintain ML pipelines, monitor models, and contribute to AI standards within the organization. 🗂️ Requirements: Minimum 1.5 years of experience in Data Science, ML Engineering or MLOps, Proficiency in Python, Experience with ML/DL libraries (scikit-learn, pandas, numpy, PyTorch or TensorFlow), Knowledge of SQL, Experience working with structured and unstructured data, Experience building and deploying ML/AI models, Knowledge of Docker, Knowledge of Git or GitHub, Understanding of CI/CD basics, Experience with MLflow, DVC or Weights & Biases, Knowledge of statistics, model validation and A/B testing, English level B2 or higher 📃 Skills: Python, SQL, scikit-learn, pandas, numpy, PyTorch, TensorFlow, Docker, Git, GitHub, CI/CD, MLflow, DVC, Weights&Biases, MachineLearning, MLOps, Statistics, ABtesting 🏢 Description: Chcesz tworzyć rozwiązania AI, które mają realny wpływ na biznes? Szukamy osoby, która swobodnie porusza się między analizą danych, Machine Learningiem i MLOps, a jednocześnie potrafi rozmawiać z biznesem i przekładać dane na konkretne decyzje oraz wdrożenia. Dołączysz do zespołu rozwijającego nowoczesne rozwiązania AI — od eksploracji danych i eksperymentów, po wdrożenia modeli i rozwój standardów AI w organizacji. Wymagania Min. 1,5 roku doświadczenia w obszarze Data Science, ML Engineering lub MLOps Bardzo dobra znajomość Python oraz bibliotek ML/DL (scikit-learn, pandas, numpy, PyTorch lub TensorFlow) Znajomość SQL oraz pracy z danymi strukturalnymi i niestrukturalnymi Doświadczenie w budowie i wdrażaniu modeli ML/AI Znajomość Docker, Git/GitHub oraz podstaw CI/CD Doświadczenie z narzędziami MLflow, DVC lub Weights & Biases Wiedza z zakresu statystyki, walidacji modeli i testów A/B Umiejętność współpracy z biznesem i prezentowania wyników analiz Analityczne myślenie, dobra organizacja pracy i nastawienie na rezultat Język angielski min. B2 Mile widziane Wykształcenie kierunkowe (informatyka, matematyka, data science lub pokrewne) Doświadczenie w pracy z politykami bezpieczeństwa danych i etyką AI Zadania na stanowisku Analiza danych i identyfikacja obszarów do wykorzystania AI oraz ML Projektowanie, trenowanie i wdrażanie modeli Machine Learning Prowadzenie eksperymentów i monitorowanie jakości modeli po wdrożeniu Rozwój pipeline’ów CI/CD oraz konteneryzacja środowisk ML z użyciem Docker Wersjonowanie modeli, danych i kodu oraz dokumentowanie prac projektowych Współpraca z zespołami Data Engineering, Backend i DevOps Prezentowanie wyników analiz oraz rekomendacji dla biznesu Udział w rozwoju standardów i rozwiązań AI w organizacji Co oferujemy Udział w nowoczesnych projektach AI i Data Science Pracę z nowoczesnym stackiem technologicznym Realny wpływ na rozwój rozwiązań analitycznych w organizacji Wynagrodzenia złożone z podstawy i premii - regulaminowej rozliczanej kwartalnie (budżet premii 10% wynagrodzenia zasadniczego kwartalnego) Możliwość doskonalenia zawodowego - finansowane przez firmę szkolenia i podnoszenie kwalifikacji Pakiet benefitów - prywatna opieka medyczna, ubezpieczenia grupowe, możliwość skorzystania z pakietu sportowego (karta Multisport) Dostęp do platformy e-learningowej z bogatą bazą kursów Imprezy integracyjne

🏢 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: B2B contract role for a Senior DevOps Engineer working on an AI initiative to build and operate a scalable, secure end-to-end AutoML platform. The position focuses on designing cloud infrastructure, automating CI/CD for model deployment, and ensuring reliable operation of distributed AI services. You will enable production-grade ML workflows with strong emphasis on scalability, security, and automation. 🗂️ Requirements: 5+ years DevOps or Cloud Engineering experience, Hands-on experience with Docker and Kubernetes, Experience with Terraform (Infrastructure as Code), Experience with CI/CD tools, Knowledge of AWS, Azure, or GCP, Experience with secure networking and identity management, Understanding of distributed systems and scalability, Experience with monitoring, logging, and alerting tools, English proficiency, Location in Poland or EU 📃 Skills: Docker, Kubernetes, Terraform, CI/CD, GitHub, GitLab, Jenkins, AWS, Azure, GCP, Networking, IAM, Monitoring, Logging, AutoML, MLOps 🏢 Description: Holisticon Insight is a data-focused technology and consulting company, part of the Nexer Group. Our mission is simple: We make companies Data Driven. We build data platforms and data-intensive systems that solve real business problems. We work end-to-end, from integrating data from multiple sources (including IoT and operational systems), through building scalable data pipelines and semantic layers, to delivering analytics and AI solutions used in production. Our projects often combine data engineering, cloud, and software development. We don’t just process data - we also build systems that generate it, such as IoT and digital platforms, which later become part of the data ecosystem. You’ll work on real use cases – optimizing processes, enabling better decisions, and building data products used by business teams. We focus on clean architecture, modern tech stacks, and ownership of what we build. At Holisticon Insight we value strong engineering skills, autonomy, and collaboration. We keep a flat structure, small teams, and a lot of responsibility on the engineer level. Check us out! 👉 https://holisticon.pl/holisticon-insight/ We offer a B2B Contract: 155 – 185 PLN net/hour + VAT We are looking for a Senior DevOps Engineer to join a cutting-edge AI initiative of our client. The project focuses on resolving data fragmentation and incompleteness by building an end-to-end AutoML pipeline that identifies missing data, trains predictive models, and automatically enriches datasets. As a DevOps Engineer, you will ensure the platform is scalable, secure, and fully automated , enabling reliable deployment and operation of AI services in a distributed computing environment. Your responsibilities: Design and manage cloud infrastructure for a distributed AI/ML platform Transition existing scripts into a scalable, production-ready environment Build and maintain CI/CD pipelines for model deployment Automate promotion of models from staging to production Ensure environment consistency and deployment reliability Containerize AI services to guarantee repeatable and consistent execution Optimize compute costs (e.g., spot instances for training workloads) Implement monitoring, logging, and alerting systems Set up alerts for performance degradation and system anomalies Collaborate closely with Data Engineers, ML Engineers, and Fullstack Developer You might be the perfect match if you possess the following competencies: 5+ years of prior experience in DevOps / Cloud Engineering Strong hands-on experience with: Docker & Kubernetes Terraform (Infrastructure as Code) CI/CD tools (GitHub Actions, GitLab CI, Jenkins, or similar) Deep knowledge of at least one cloud platform: AWS, Azure, or GCP Experience with secure networking, identity management, and cloud architecture Solid understanding of distributed systems and scalability challenges Experience with monitoring, logging, and alerting tools Strong problem-solving skills and ownership mindset Excellent English communication skills Location in Poland or EU 🙌 Nice to have: Experience with MLOps workflows and model lifecycle management Familiarity with AutoML pipelines or AI platforms By joining Holisticon Insight you will get: Life insurance Multisport card Fully remote job Private medical care Flexible working hours B2B or contract of employment Amazing integration events on a regular basis Training budget (e.g. Microsoft Azure Certifications) Opportunity to impact our company culture build-up Work equipment (laptop, 2 monitors, and accessories)

🏢 Summary: Backend Engineer role focused on designing and developing a cloud-native SaaS platform for e-commerce data processing, combining Python-based backend architecture with AI agent workflows. The position involves building scalable REST APIs, orchestrating AI-driven processes, and working extensively with Google Cloud and analytical data systems. This is a hands-on engineering role with strong impact on system architecture, scalability, and data-intensive solutions. 🗂️ Requirements: Minimum 3–4 years of commercial experience with Python and web frameworks (FastAPI, Django or Flask), Experience with Google Cloud Platform or strong experience with AWS/Azure and willingness to work with GCP, Practical experience with RESTful API design and backend development, Strong knowledge of PostgreSQL and relational databases, Understanding of analytical databases such as BigQuery, Practical knowledge of SOLID principles and Dependency Injection, Experience with asynchronous programming (asyncio), Experience with CI/CD processes and application deployments, Ability to design scalable and secure system architecture, Experience working with AI-assisted development tools 📃 Skills: Python, FastAPI, SQLAlchemy, Pydantic, GCP, CloudRun, CloudSQL, BigQuery, CloudStorage, PostgreSQL, REST, SQL, asyncio, CI/CD, Terraform, OAuth, LangChain, LangGraph, ADK, ETL 🏢 Description: 🐙 Cześć! Jesteśmy niewielką, ale dynamicznie rosnącą firmą 💡 z innowacyjnym produktem dla e-commerce w modelu SaaS 🛍️. Data Octopus to platforma do zarządzania danymi o klientach i produktach dla e-commerce. Umożliwia zespołom odpowiedzialnym za digital marketing szybsze i skuteczniejsze przygotowywanie danych dla platform reklamowych oraz osiąganie lepszych wyników na poziomie przychodów oraz zysku. Wśród naszych klientów znajdziesz liderów swoich kategorii oraz szybko rozwijające się sklepy 🏆, takie jak Selsey, Pitbull, R-gol, X-kom. Nasza aplikacja w całości działa w Google Cloud ☁️, a jej natywną hurtownią danych jest Google BigQuery 🔄. ✨ Co nas cechuje i dlaczego warto? Architektura, nie tylko kod: Jesteśmy małym, zwartym zespołem — każda osoba ma realny wpływ na to, jak wygląda system. Szukamy kogoś, kto potrafi projektować architekturę i myśleć o konsekwencjach decyzji technicznych, a nie tylko implementować według cudzej specyfikacji. AI-first od środka: Nie mówimy o AI przy kawie — budujemy produkt, który aktywnie wykorzystuje agenty i workflow agentowe jako core funkcjonalności. Narzędzia takie jak Cursor czy Claude Code to u nas codzienność, nie eksperyment. Zero legacy, zero on-premise: Cały stack to Google Cloud Platform — Cloud Run, BigQuery, Cloud SQL. Żadnego długu technologicznego, żadnych serwerów w piwnicy. Blisko biznesu i feedbacku: Współtworzysz produkt używany przez liderów e-commerce (Selsey, Pitbull, R-gol, X-kom). Widzisz, jak Twoje decyzje przekładają się na wyniki klientów. Elastyczny model pracy (stacjonarny, hybrydowy, full remote): profesjonalny onboarding oraz benefity: dofinansowanie Multisport, LuxMed, wysokiej jakości sprzęt. 🧑‍🏭 Kogo szukamy? W Data Octopus budujemy analityczny “mózg” dla e-commerce. Nie doklejamy API do frontendu – tworzymy zaawansowany system SaaS, który przetwarza miliony rekordów i jest aktywnie wykorzystywany do poprawy zysku dla naszych klientów. Szukamy inżyniera, a nie tylko programisty. Dołączysz do zespołu, w którym Cloud Native (GCP), to codzienność, a nie buzzword. Zero legacy on-premise. Dlaczego teraz? Wchodzimy w nowy etap rozwoju produktu — budujemy backend, który jest czymś więcej niż klasycznym API. To hybryda kodu backendowego i silnika zasilającego workflow agentowe: funkcje backendowe generują kontekst, który trafia do agentów AI, a backend orkiestruje i obsługuje te workflow end-to-end. Szukamy kogoś, kto rozumie zarówno solidną architekturę backendową, jak i to, jak projektować systemy z myślą o agentach — i kto chce współtworzyć to od środka. 🎯 Twoje zadania: Projektowanie i rozwój warstwy backendowej oraz API (RESTful) przy użyciu najnowszych standardów Python 3.12 i FastAPI. Tworzenie rozwiązań Cloud Native na Google Cloud Platform – będziesz aktywnie korzystać z Cloud Run, Cloud SQL, BigQuery czy Cloud Storage. Aktywny udział w projektowaniu architektury systemu – masz realny wpływ na decyzje dotyczące skalowalności, wydajności i bezpieczeństwa platformy przetwarzającej miliony rekordów. Implementacja nowych modułów biznesowych od podstaw, przekładając wymagania biznesowe na konkretne, wydajne rozwiązania techniczne. Praca z danymi – projektowanie i optymalizacja interakcji z bazami PostgreSQL (Cloud SQL) oraz systemem analitycznym BigQuery , wspierając procesy data-driven. Dbanie o wysoką jakość kodu ( Clean Code, SOLID, type hints ) – tworzenie czytelnych, łatwych w utrzymaniu rozwiązań oraz przeprowadzanie Code Review, by wspólnie podnosić standardy w zespole. Obsługa procesów CI/CD i wdrożeń – odpowiedzialność za deploy aplikacji na środowiska testowe i produkcyjne oraz dbanie o płynność dostarczania nowych wersji. 💡 Poszukiwane umiejętności: 💻 Główny Tech Stack: Nie wymagamy znajomości każdej biblioteki, której używamy, ale powinieneś swobodnie poruszać się w tym środowisku: Core: Python 3.12, FastAPI, SQLAlchemy (async), Pydantic Cloud (GCP): Cloud Run (Serverless), Cloud SQL (PostgreSQL), BigQuery, Cloud Storage, Secret Manager Data: BigQuery (Google SQL), PostgreSQL, Google Cloud Storage Architektura: Modular Monolith, Clean Architecture, async-first, Test-Driven Development, Spec-Driven Development 🔍 Czego oczekujemy? Szukamy osoby z podejściem “product-minded”, która rozumie biznesowy cel kodu. Doświadczenie: Min. 3-4 lata komercyjnej pracy z Python i frameworkami webowymi (FastAPI, Django lub Flask). Chmura: Znajomość Google Cloud Platform (lub solidne doświadczenie w AWS/Azure i chęć szybkiego wejścia w świat Google). Bazy danych: Dobre zrozumienie różnic i zastosowań baz relacyjnych (PostgreSQL) oraz analitycznych (BigQuery). Wzorce: Praktyczna znajomość SOLID, Dependency Injection oraz asynchroniczności (asyncio). AI Engineering: Naturalnie korzystasz z narzędzi AI wspomagających development (Cursor, Claude Code lub podobnych) — nie jako zamiennika myślenia, lecz jako codziennego narzędzia, które pozwala działać szybciej i lepiej. Interesuje Cię, jak budować oprogramowanie z pomocą agentów AI, a nie tylko obok nich. Mile widziane — AI & Agenty: Znajomość LangChain / LangGraph / Google ADK oraz doświadczenie w budowaniu rozwiązań opartych na agentach AI (MCP, narzędzia, skille – nie tylko korzystanie z gotowych asystentów). Interesuje Cię, jak projektować backend, który aktywnie zasila workflow agentowe. Mile widziane — Cloud & Integracje: Doświadczenie z Terraform (IaC) oraz implementacja OAuth pod kątem budowania connectorów do zewnętrznych platform. Mile widziane — Data Engineering: Doświadczenie z data quality i pipeline’ami ETL/ELT — jeśli pracowałeś z jakością danych, rozumiesz, dlaczego testy i code review to nie biurokracja, a fundament. ⚙️ Jak wygląda proces rekrutacyjny? Krótka rozmowa telefoniczna. Spotkanie online. Zadanie rekrutacyjne. Spotkanie w biurze z Head of Data & Product Data Octopus - Rafałem Idzikiem, podczas którego porozmawiamy o Twoim dotychczasowym doświadczeniu, roli oraz omówimy zadanie rekrutacyjne.

🏢 Summary: The offer is for a DevOps Engineer to build and operate scalable, secure AWS infrastructure for a cloud-native AI Talent Intelligence Platform. The role focuses on production-grade infrastructure, CI/CD automation, observability, security, cost optimization, and ML/AI workload support. It involves full ownership of AWS environments across development and production with strong emphasis on reliability and compliance. 🗂️ Requirements: 5+ years of production infrastructure experience, 3+ years of hands-on AWS experience, Strong experience with AWS services: RDS Postgres, VPC, IAM, ECS or EKS, ALB/NLB, Route53, S3, CloudFront, CloudWatch, Secrets Manager, Advanced Terraform usage: modules, remote state, drift detection, multi-environment setups, Experience building and maintaining CI/CD pipelines (GitHub Actions or equivalent), Proficiency with Linux and containerization, Experience implementing observability: metrics, logs, traces, alerting, On-call and incident management experience, PostgreSQL operations knowledge: backups, migrations, performance tuning, Security best practices: IAM policies, secret management, encryption, AWS cost monitoring and optimization, English proficiency Intermediate+ or higher 📃 Skills: AWS, RDS, PostgreSQL, EC2, ECS, EKS, VPC, IAM, Route53, S3, CloudFront, CloudWatch, SecretsManager, ACM, SES, Terraform, GitHubActions, Linux, Docker, Grafana, Datadog, CI/CD, ALB, NLB, TLS 🏢 Description: Andersen is hiring a DevOps Engineer in the EU for a project building cloud-native AI platforms and ensuring scalable, secure, and reliable infrastructure. The customer is an international company focused on delivering innovative digital solutions and technology-driven services. The organization supports businesses in improving operational efficiency, streamlining processes, and accelerating digital transformation initiatives. By leveraging modern technologies and industry best practices, the company develops scalable and secure solutions tailored to evolving business needs. The customer operates in a dynamic environment, emphasizing innovation, collaboration, and the continuous enhancement of products and services to create long-term value for its clients. The project is focused on developing a cloud-native Talent Intelligence Platform powered by AI and machine learning technologies. It includes building scalable AWS-based infrastructure, supporting intelligent search and ranking capabilities, and ensuring secure, reliable, and observable platform operations. Responsibilities: Owning AWS infrastructure across dev and prod — RDS (Postgres), EC2/ECS/EKS workloads, VPC, IAM, Route53, S3, CloudFront, Secrets Manager, ACM, SES, CloudWatch. Keeping dev and prod in lockstep — same Terraform modules, same patterns, real environment parity. Running CI/CD — GitHub Actions pipelines across several service repos (backend, frontend, AI/ML, gateway, billing). Trunk-based, feature-flagged, main always deployable. Codifying infrastructure — Terraform for everything that touches AWS. No click-ops. Managing observability — metrics, logs, traces, alerting. CloudWatch + Grafana/Datadog (your call, with budget). SLOs that matter. Participating in incident response — on the rotation. Writing runbooks. Leading blameless postmortems. Managing cost control — monthly bill review, right-sizing, Savings Plans / RIs, S3 lifecycle, RDS tuning. Catching anomalies before finance does. Ensuring security & compliance — IAM least-privilege, secrets rotation, vulnerability scanning, GDPR-aligned data handling with EU-resident third-party providers, backup/DR testing. Supporting ML/AI infrastructure — model serving latency budgets, embedding storage, GPU/CPU tradeoffs, A/B infrastructure for ranking experiments. Managing database operations — RDS upgrades, parameter tuning, backward-compatible migrations, read replicas, PITR. Must-have: Experience running production infrastructure for 5+ years. Experience working with AWS for 3+ years. Hands-on with: RDS Postgres, VPC networking, IAM, ECS or EKS, ALB/NLB, Route53, S3, CloudFront, CloudWatch, Secrets Manager. Terraform in anger — modules, remote state, drift detection, multi-environment. CI/CD — GitHub Actions or equivalent. Linux + containers — Docker, image hygiene, sensible base images, multi-stage builds. Observability — metrics, logs, traces, and alerting. Incident command — on-call experience, leading incidents, writing postmortems. PostgreSQL operations — backups, migrations, query performance basics, RDS quirks. Security fundamentals — IAM policy writing, secret management, TLS, encrypted-at-rest by default. Cost awareness — monitoring and optimizing AWS infrastructure costs. Level of English – from Intermediate+ and above. Nice to haves: Experience with Keycloak in production environments. Experience with Vespa, Elasticsearch, OpenSearch, or other search infrastructure. Experience with AWS Bedrock or other managed AI tooling. Experience with ML serving using SageMaker, Bedrock, or self-hosted GPU inference. Experience with GDPR compliance and EU data residency requirements. Experience with Helm, Kustomize, and ArgoCD. FinOps mindset — tagging strategy, showback, and anomaly detection. Reasons why this job would be interesting to you: Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc.. The opportunity to change the project and/or develop expertise in an interesting business domain. Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee. The opportunity to earn up to an additional 1,000 USD per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities. Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated. Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies). Certification compensation (AWS, PMP, etc). Referral program. Private health insurance and compensation for sports activities. Your personal data is protected in accordance with GDPR regulations. Learn more: https://andersenlab.com/privacy-policy/pl Join us! https://people.andersenlab.com/

🏢 Summary: The offer is for a Software Engineer (Data) role focused on building scalable data infrastructure and production-ready ML systems within an AI-driven environment. The position combines backend engineering, data engineering, and MLOps to design high-throughput pipelines and support AI solutions from experimentation to production. The role involves close collaboration with Data Science teams to shape architecture and engineering standards for next-generation data platforms. 🗂️ Requirements: 4+ years of software engineering experience, Strong proficiency in Python, Experience building scalable production systems, Experience with data-intensive applications and SQL databases, Knowledge of data modeling and query optimization, Experience with Terraform, Kubernetes, Docker or similar tools, Understanding of CI/CD pipelines and Infrastructure as Code, Experience implementing automated testing and clean architecture principles, Ability to productionize ML solutions with Data Science teams 📃 Skills: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker, Airflow, SQL, CI/CD, AWS, Snowflake, DBT, MLOps 🏢 Description: Our client helps small teams power big businesses with the must-have platform for intelligent marketing automation. Customers from over 170 countries depend on the Client’s mix of pre-built automation and integration to power personalized marketing, transactional emails, and one-to-one CRM interactions throughout the customer lifecycle. We’re looking for a Software Engineer (Data) to join our client’s growing AI and Data organization and help build the scalable foundations behind next-generation data and ML systems. In this role, you won’t just be working with data infrastructure; you’ll be shaping the software architecture, engineering standards, and production-ready platforms that power the company’s AI ecosystem. This is more than a traditional backend or data engineering position. You’ll work at the intersection of software engineering, data, and AI, partnering closely with Data Science and AI teams to bridge the gap between experimentation and production. From designing resilient systems to building scalable MLOps pipelines, your work will directly influence how AI solutions are developed, deployed, and scaled across the organization. Key takeaways: Stack: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker Salary : 20 000 - 23 000 PLN gross/month, Contract of employment (+10% annual bonus, 75% Creative Tax) Working model: Hybrid, once a week in the office Location: Krakow, ul. Konopnickiej Recruitment process: Call with MOTIFE recruiter (30 min) Interview with Hiring Manager (45 min) Technical interview, live coding (1h) Cross-functional interview (1h) Responsibilities: Design, develop, and maintain scalable high-throughput data pipelines across complex data ecosystems. Build and optimize data models, schemas, and database structures to ensure long-term scalability and performance. Implement engineering best practices, including automated testing, CI/CD pipelines, and Infrastructure as Code (Terraform). Partner closely with AI and Data Science teams to productionize machine learning solutions and develop scalable MLOps pipelines. Engineer and maintain feature stores and data infrastructure supporting AI-driven initiatives. Monitor, maintain, and improve the reliability and efficiency of containerized environments using Kubernetes, Docker, and Airflow. Ensure platform stability, observability, and operational excellence through proactive system monitoring and health checks. Collaborate cross-functionally with engineering and business stakeholders to translate complex technical concepts into actionable insights. Contribute to the architectural direction and scalability of the organization’s AI and data platforms. Drive the adoption of robust software engineering standards across data and infrastructure projects. Requirements: 4+ years of experience in software engineering, with strong hands-on experience in backend development and building scalable production systems. Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, and maintainable code practices. Experience working with data-intensive applications, databases, and SQL, including data modeling and query optimization. Exposure to modern data engineering, cloud, or infrastructure environments, with familiarity in tools such as Terraform, Kubernetes, Docker, or similar technologies. Understanding of CI/CD pipelines, Infrastructure as Code, and general engineering best practices. Interest in AI/ML ecosystems and willingness to work closely with Data Science and AI teams on productionizing ML solutions. Familiarity with cloud platforms and modern data stack technologies such as AWS, Snowflake, Spark, or DBT is considered a strong plus. Ownership mindset and comfort working in evolving, fast-moving environments where systems and processes are still being built. What we offer: Health Benefits 1. Medical Full coverage for employees and their dependents through LUX MED. Employees have access to the “Premium” package, providing enhanced coverage and greater access to care. A client pays 100% of the premium for employees and 50% for dependents. 2. Dental No additional cost for dental coverage- integrated into LUX MED medical plan. 3. Vision Reimbursement for vision expenses up to 400 PLN every 2 years. Mental Health Tools Access to TELUS Health EAP to provide support and resources in a time of need. Additional Benefits 10% annual bonus 75% Creative Tax Vacation: 26 days. Home Office Stipend: One-time $150 equivalent home office stipend to outfit their home office. Calm Subscription: Premium subscription access to Calm, the #1 app for sleep, meditation, and relaxation. Hub Perks: Receive meal and transportation benefits when traveling to the Poland Hub. Baby Swag: If you have a baby or adopt, you’ll receive a company-branded first bath bundle. Sabbatical Program: After 5 years of employment, receive a month-long paid sabbatical leave, with a sabbatical leave bonus.