Senior Python 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: Hands-on ML Engineer role to evolve a computer vision mobile app from prototype to production, improving model accuracy and building scalable ML infrastructure. You will design automated pipelines for adding new species, optimize models for on-device deployment, and extend support to Android. The role focuses on end-to-end ML lifecycle ownership, from training to mobile deployment. 🗂️ Requirements: 5+ years of ML engineering experience, Strong experience with PyTorch, Experience with vision transformers (ViT/timm), Experience with YOLO or Ultralytics, Experience with full ML lifecycle (training, evaluation, export, deployment), Experience deploying models to CoreML or TFLite, Experience building ML pipelines with versioning and experiment tracking, Experience implementing CI/CD for ML workflows, Experience working with small or noisy datasets, Experience designing scalable classification systems, Experience with orchestration tools (Airflow, Prefect, or Dagster) 📃 Skills: Python, PyTorch, ViT, timm, YOLO, Ultralytics, CoreML, TFLite, Airflow, Prefect, Dagster, CI/CD, MediaPipe, Quantization, Pruning, Distillation 🏢 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/ Our client is building a mobile app that uses computer vision to identify fish species, classify sex, and estimate length from photos. We’ve proven the concept — species classification at 87.6% accuracy, a length regression model with 73% improvement over baseline, and CoreML export running on iOS. Now we need someone to take this from working prototype to production-grade ML system. The founder has built the initial models and pipeline in Python, and your job is to improve model accuracy, build the infrastructure to support continuous improvement, and extend deployment to Android. This is a hands-on, build-it-yourself role — not a management position. What You’ll Own Design and build a repeatable, scalable process for adding new fish species to the classifier. Today, adding a species involves manual image scraping, hand-curated CSVs, and ad hoc retraining. We need a system where a new species can go from “we want to support this” to “it’s live on device” with minimal manual effort. What We’re Looking For 5+ years of experience in ML engineering, with meaningful time spent on both model development and production infrastructure. Specifically: Strong PyTorch experience, ideally with vision transformers (ViT/timm) and object detection (YOLO/ultralytics) Familiarity with the full model lifecycle — training, evaluation, export, and on-device deployment Hands-on experience deploying models to mobile (CoreML and/or TFLite); you don’t need to be an iOS/Android app developer, but you need to produce optimized model artifacts that mobile engineers can integrate Experience building ML pipelines with proper versioning, experiment tracking, and CI/CD Comfort working with small, noisy datasets and knowing how to get the most out of limited labeled data Experience designing classification systems that scale — adding new classes without full retraining or accuracy regression Familiarity with at least one orchestration tool (Airflow, Prefect, Dagster) Self-directed — you’ll be working with a small team and need to make pragmatic architecture decisions without a lot of oversight Nice to Have Experience with MediaPipe or similar pose/landmark detection frameworks Background in ecological or biological image classification Experience with on-device A/B testing frameworks Familiarity with edge inference optimization (quantization, pruning, distillation) Experience with few-shot learning or other techniques for bootstrapping new classes with limited data

🏢 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: The role involves owning the full MLOps lifecycle for an AI-powered sports highlights platform, from model research and training to production deployment and scalable cloud infrastructure. You will build and maintain ML pipelines, automate workflows across Computer Vision and NLP, and ensure reliable CI/CD processes in a high-uptime environment. 🗂️ Requirements: 4+ years experience in MLOps, DevOps, ML Engineering or similar role, Experience in large-scale, high-uptime production cloud environments, Strong knowledge of Linux, Experience with Docker, Experience with Kubernetes, Experience building CI/CD pipelines for distributed systems, Proficiency in Python scripting, Experience with Terraform, Understanding of machine learning fundamentals and frameworks 📃 Skills: Python, Linux, Docker, Kubernetes, Terraform, CI/CD, ClearML, W&B, SageMaker, Azure, ComputerVision, NLP, MLOps 🏢 Description: We’re seeking a skilled and motivated MLOps Engineer to join a fast-moving team building an AI-powered platform for automatic sports highlights generation. In this role, you’ll take ownership of the full MLOps lifecycle—from research and model training to production deployment and scalable infrastructure. You’ll design and maintain robust ML pipelines, automate workflows across Computer Vision, NLP, and Data Science, and build reliable CI/CD processes that keep high-uptime systems running smoothly. Your work will help deliver cutting-edge AI experiences at cloud scale, powering innovation and performance across the entire platform. Responsibilities Own and manage every aspect in the MLOps life cycle for our AI-based automatic sports highlights generation platform Design, implement and maintain our whole ML infrastructure, from research to production and from model training to data engineering Automate and innovate workflows such as serving and training pipelines for multidisciplinary ML algorithms including Computer Vision, NLP and Data Science Build and maintain CI/CD pipelines, releases and Source Code workflows Requirements 4+ years of experience as an MLOps engineer, DevOps engineer, ML Engineer, or in a similar field Experience in a large, complex, large-scale, high-uptime production Cloud environment Core understanding of Linux OS, Docker components, and Kubernetes Experience with CI/CD pipelines for distributed production systems Experience with Python scripting Work experience with Terraform Working with MLOps platforms such as Experiment Tracking, Model Registry, feature Store - an advantage (e.g ClearML , W&B, Aws Sagemaker) Highly motivated, goal-driven, innovative, curious, and open-minded Will be a plus Working with Azure Cloud in a high-scale production environment Understanding of AI and machine learning fundamentals, concepts and frameworks What we offer Competitive salary range Medical insurance Paid vacation and sick leaves MultiSport card Top equipment kit, co-workings Hybrid set of works (Office location: Warsaw) Collaborative and innovative work environment Career growth and development opportunities A chance to work with giants of the sports industry About the project Our partner leads the industry in generating dynamic sports videos for every digital destination. Their cutting-edge AI and Machine Learning technologies analyse live sports broadcasts from over 250 leagues and broadcast partners, including iconic names like the NBA, NHL, ESPN, FIBA and Bundesliga, to create personalized, short-form videos in real-time. The solution empowers media rights owners to unlock new revenue streams and deliver a tailored fan experience across every digital platform. Join the high-profile Engineering team and discover the forefront of sports content innovation.

🏢 Summary: Long-term B2B contract for an MLOps/LLMOps Engineer to design and build a scalable AI/LLM platform in Azure and Kubernetes environments. The role focuses on automating ML lifecycle processes, managing production-grade ML systems, and ensuring observability and compliance with AI regulations. Hybrid work model with at least one day per week onsite in Warsaw. 🗂️ Requirements: Minimum 3 years of commercial experience in DevOps, MLOps or ML production engineering, Strong knowledge of Kubernetes and Docker including cluster management and Helm charts, Experience with public cloud Azure or GCP or AWS, Experience in Python scripting, Experience in Bash or Shell scripting, Experience with ML lifecycle management tools such as MLflow or Kubeflow, Experience with Infrastructure as Code tools such as Terraform or Bicep or Ansible, Ability to provide services from Poland 📃 Skills: Azure, AzureML, AKS, Kubernetes, Docker, Helm, Python, Bash, MLflow, Kubeflow, Terraform, Bicep, Ansible, GCP, AWS, DVC, CICD 🏢 Description: MLOps / LLMOps Engineer (Azure & Kubernetes) Dla naszego klienta – lidera sektora ubezpieczeń, który rewolucjonizuje swoje usługi za pomocą AI – szukamy inżyniera gotowego wziąć na klatę budowę nowoczesnej platformy LLMOps pod najnowsze wymogi AI Act. Jeśli automatyzację stawiasz na pierwszym miejscu, a środowiska produkcyjne z modelami ML to Twój chleb powszedni – czytaj dalej! ⚡ Kluczowe informacje: Forma współpracy: Kontrakt B2B, Długofalowy Model pracy: Hybryda (Warszawa, Wola, blisko komunikacji miejskiej – min. 1 dzień w tygodniu w biurze) 🛠️ Twój codzienny stack & wyzwania: Infrastruktura AI/LLM: Projektowanie i budowa skalowalnych środowisk w oparciu o Azure Machine Learning, Azure AI Foundry oraz Kubernetes (AKS). Automatyzacja (CI/CD/CT): Wdrażanie potoków dla ML, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training. Observability: Monitoring modeli pod kątem Data Drift/Model Drift na produkcji. Architektura: Konteneryzacja (Docker), zarządzanie klastrami (Helm) oraz praca na styku systemów chmurowych i on-premise. 🎯 Czego oczekujemy? (Must Have): Min. 3 lata komercyjnego doświadczenia w obszarze DevOps, MLOps lub inżynierii oprogramowania z modelami ML na produkcji Bardzo dobrej znajomości Kubernetes & Docker (zarządzanie klastrami, Helm charts). Doświadczenia z chmurą publiczną (głęboki Azure lub GCP/AWS, jeśli chcesz szybko wejść w Azure). Praktyki w pisaniu skryptów w Python oraz Bash/Shell. Znajomości narzędzi do zarządzania cyklem życia modeli (np. MLflow , Kubeflow) oraz podejścia Infrastructure as Code (Terraform, Bicep lub Ansible). Świadczenia usług z terytorium Polski. ⭐ Mile widziane (Nice to Have): Doświadczenie we wdrażaniu modeli LLM i architektur RAG . Znajomość baz wektorowych (np. Azure AI Search) oraz narzędzi do monitoringu (Prometheus, Grafana). Certyfikaty: Azure DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102). Oferujemy: Preferencyjne pakiety do wykupienia na Multisport i Luxmed. Współpracę w ramach kontraktu B2B. 👋 Brzmi jak Twój kolejny krok zawodowy? Aplikuj!

🏢 Summary: B2B role for an MLOps/LLMOps Engineer to design and build a scalable LLM platform on Azure and Kubernetes, ensuring automated CI/CD/CT pipelines and production-grade ML model operations. The position focuses on AI infrastructure, model lifecycle management, observability, and hybrid cloud/on-prem integration. Hybrid work model with long-term cooperation. 🗂️ Requirements: Minimum 3 years of commercial experience in DevOps, MLOps or ML engineering in production environments, Strong hands-on experience with Kubernetes and Docker, including cluster management and Helm, Experience with public cloud (Azure preferred, or GCP/AWS with readiness to work on Azure), Proficiency in Python and Bash/Shell scripting, Experience with ML lifecycle tools such as MLflow or Kubeflow, Experience with Infrastructure as Code tools (Terraform, Bicep or Ansible), Ability to provide services from Poland 📃 Skills: Azure, AzureML, AKS, Kubernetes, Docker, Helm, Python, Bash, MLflow, Kubeflow, Terraform, Bicep, Ansible, DVC, Prometheus, Grafana, GCP, AWS 🏢 Description: MLOps / LLMOps Engineer (Azure & Kubernetes) Dla naszego klienta – lidera sektora ubezpieczeń, który rewolucjonizuje swoje usługi za pomocą AI – szukamy inżyniera gotowego wziąć na klatę budowę nowoczesnej platformy LLMOps pod najnowsze wymogi AI Act. Jeśli automatyzację stawiasz na pierwszym miejscu, a środowiska produkcyjne z modelami ML to Twój chleb powszedni – czytaj dalej! ⚡ Kluczowe informacje: Forma współpracy: Kontrakt B2B, Długofalowy Model pracy: Hybryda (Warszawa, Wola, blisko komunikacji miejskiej – min. 1 dzień w tygodniu w biurze) 🛠️ Twój codzienny stack & wyzwania: Infrastruktura AI/LLM: Projektowanie i budowa skalowalnych środowisk w oparciu o Azure Machine Learning, Azure AI Foundry oraz Kubernetes (AKS). Automatyzacja (CI/CD/CT): Wdrażanie potoków dla ML, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training. Observability: Monitoring modeli pod kątem Data Drift/Model Drift na produkcji. Architektura: Konteneryzacja (Docker), zarządzanie klastrami (Helm) oraz praca na styku systemów chmurowych i on-premise. 🎯 Czego oczekujemy? (Must Have): Min. 3 lata komercyjnego doświadczenia w obszarze DevOps, MLOps lub inżynierii oprogramowania z modelami ML na produkcji Bardzo dobrej znajomości Kubernetes & Docker (zarządzanie klastrami, Helm charts). Doświadczenia z chmurą publiczną (głęboki Azure lub GCP/AWS, jeśli chcesz szybko wejść w Azure). Praktyki w pisaniu skryptów w Python oraz Bash/Shell. Znajomości narzędzi do zarządzania cyklem życia modeli (np. MLflow , Kubeflow) oraz podejścia Infrastructure as Code (Terraform, Bicep lub Ansible). Świadczenia usług z terytorium Polski. ⭐ Mile widziane (Nice to Have): Doświadczenie we wdrażaniu modeli LLM i architektur RAG . Znajomość baz wektorowych (np. Azure AI Search) oraz narzędzi do monitoringu (Prometheus, Grafana). Certyfikaty: Azure DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102). Oferujemy: Preferencyjne pakiety do wykupienia na Multisport i Luxmed. Współpracę w ramach kontraktu B2B. 👋 Brzmi jak Twój kolejny krok zawodowy? Aplikuj!

🏢 Summary: Opportunity for a Lead Full Stack Engineer to provide technical leadership while building scalable, high‑performance applications using the Microsoft stack and modern cloud architecture. The role combines hands-on full stack development with active adoption of AI-assisted engineering practices across the SDLC. You will drive engineering excellence, mentor teams, and integrate AI capabilities into products and internal tools. 🗂️ Requirements: 7+ years experience with Microsoft stack (.NET, C#, SQL Server, T-SQL), Strong experience with .NET Core and Angular, Proficiency in JavaScript, HTML, CSS, Experience with Git, Experience designing scalable and maintainable systems, Knowledge of SOLID principles and design patterns, Experience with microservices architecture, Experience with Azure or other cloud platforms, Experience working in Scrum environments, Hands-on use of GenAI tools (Copilot, ChatGPT, Cursor) in SDLC, Experience integrating LLM APIs or AI capabilities into applications, Understanding of AI risks, testing, reviews, and compliance guardrails 📃 Skills: .NET, C#, SQL, T-SQL, .NETCore, Angular, JavaScript, HTML, CSS, Git, Azure, Microservices, SOLID, GenAI, LLM, Copilot, ChatGPT, Cursor 🏢 Description: About the Client: The Client provides comprehensive operational support and a range of expert services to the world’s leading insurers, brokers, fleet managers and automotive manufacturers. 3,300 employees across ten countries deliver exceptional standards on a large scale for over 1,200 clients. We help the global insurance market to handle millions of claims each year in the most cost-effective and efficient ways possible. The Client is embarking on an exciting and challenging transformation program and our software solutions are a driving force behind this strategy using cloud computing and leading-edge design patterns. Short Role Description: This is a great opportunity for an experienced Lead Full Stack Engineer to join our Software Development team as part of one of our established Scrum teams. Each agile team includes technical leadership, development, architecture, QA, product, and project management roles. We are looking for a strong engineering leader who combines deep hands-on development expertise with an AI-first mindset. This role requires an AI Champion — someone who actively applies AI tools across the Software Development Life Cycle and helps elevate engineering productivity, quality, and delivery standards through practical and responsible AI adoption. You will not only build high-performance applications, including high-traffic customer-facing platforms, but also act as a multiplier for engineering excellence by introducing modern practices, tools, and ways of working. Responsibilities: Provide technical leadership, mentorship, and hands-on support to engineers within the team. Design, develop, test, document, and maintain scalable backend and frontend solutions. Deliver high-quality, reusable, and maintainable code aligned with best engineering practices. Actively apply AI-assisted development approaches across design, coding, testing, and debugging. Drive improvements in engineering efficiency, quality, and delivery through practical AI usage. Produce clear and structured technical documentation. Provide realistic estimates and manage delivery timelines effectively. Participate in all Scrum ceremonies and contribute to team performance and continuous improvement. Coach teams on effective usage of GenAI tools and promote AI-first engineering culture. Requirements: 7+ years of experience with Microsoft development stack (.NET / C# / SQL Server / T-SQL). Strong experience with .NET Core, Angular, JavaScript, HTML, CSS, Git. Hands-on experience designing scalable and maintainable systems. Solid understanding of SOLID principles, design patterns, and microservices architecture. Experience working with cloud platforms (Azure preferred). Strong experience working in Scrum environments across different team setups. AI & Engineering Excellence Requirements: Proven hands-on experience using GenAI tools (e.g., Copilot, ChatGPT, Cursor, etc.) to improve development speed, code quality, testing, debugging, and documentation. Practical experience integrating LLM APIs or AI capabilities into applications or internal tools. Understanding of AI limitations, risks, and guardrails (testing, reviews, policies, compliance). Ability to introduce new tools and practices, influence teams, and scale adoption across engineering. Nice to Have: Experience building internal developer productivity tools or engineering automation solutions. Exposure to performance, scalability, and security requirements for large-scale platforms. Personal Attributes: Self-organising problem solver. Able to think outside the box (open thinking). Strong ability to work effectively in a team. Collaborating, motivating and energizing others (Team player). Encourage and drive others to deliver high-quality, cutting-edge software. You will have attention to detail and have a proactive ethos. Always looking for continuous improvement. Excellent verbal and written communication skills, in English. We offer*: Flexible working format - remote, office-based or flexible A competitive salary and good compensation package Personalized career growth Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more) Active tech communities with regular knowledge sharing Education reimbursement Memorable anniversary presents Corporate events and team buildings Other location-specific benefits

🏢 Summary: B2B contract role for a Data/MLOps Engineer responsible for bridging data science and production by building and maintaining scalable ML and data pipelines on AWS. The position focuses on deploying end-to-end ML lifecycles, optimizing big data processing with Spark, and implementing robust MLOps practices. The engineer will work closely with Data Scientists to productionize models and ensure reliable, high-performance cloud infrastructure. 🗂️ Requirements: Strong ML background, Experience productionizing ML models, Proficiency in Python, Strong PySpark skills, Hands-on experience with AWS SageMaker, Experience with Apache Spark, Experience building ETL/ELT pipelines, Experience with Infrastructure as Code, Experience implementing CI/CD pipelines, Knowledge of model monitoring and drift detection, Experience working in Agile environment, English level B2 minimum 📃 Skills: Python, PySpark, PyTorch, SQL, Spark, ETL, ELT, AWS, SageMaker, CDK, Lambda, Terraform, CloudFormation, CI/CD, MLOps, IaC 🏢 Description: co.brick talents — powered by AI, powered by people. Data/MLOps Engineer (CT&C Engineering) For our Client, we are looking for a Data/MLOps Engineer to join their CT&C Engineering team. In this role, you will bridge the gap between data science and production, ensuring that scalable data solutions provide efficient ingestion, transformation, storage, and real-time analysis. If you have a strong background in ML, solid PySpark skills, and know AWS SageMaker inside out, this role is for you! Quick Job Details Rate: 140 – 150 PLN/h net Form of Cooperation: B2B Contract Start Date: ASAP English: Minimum B2 level Who Our Client Is Looking For We need a technical expert who brings overall ML background knowledge and can specifically address these core needs: The Bridge to Production: You can confidently face off with Data Scientists (who often produce notebooks only) and successfully implement their work into production-quality models. ML Model Expertise: You understand different ML models, know how to monitor them, and clearly understand their pros and cons. Hands-on Implementation: You are technically capable of building and executing these solutions using PySpark and AWS SageMaker. Technical Stack Languages & Frameworks: Python, PySpark, PyTorch, SQL Data Processing: Apache Spark, ETL/ELT Cloud & Infrastructure: AWS CDK, AWS Lambdas, AWS SageMaker, Terraform / CloudFormation Methodology & Tools: Agile, CI/CD, Training Design Key Responsibilities 1. ML & Data Infrastructure Deploy and maintain end-to-end ML lifecycles (automated training, deployment, and versioning). Build and support core MLOps components like Feature Stores, experiment tracking, and model registries. Manage scalable cloud infrastructure using Infrastructure as Code (IaC) and develop robust CI/CD/CT (Continuous Training) pipelines. 2. Data Engineering & Pipeline Optimization Build high-volume ingestion and processing pipelines using Apache Spark and PySpark. Implement data models and storage optimizations for low-latency inference and high-performance analytics. Integrate automated data quality checks and observability. 3. Governance, Security & Collaboration Proactively monitor model drift, data quality, and system latency. Maintain strict versioning for data, code, and artifacts to guarantee 100% reproducibility. Operate within an Agile framework, collaborate with Data Scientists and Product Owners, and provide clear technical documentation.

🏢 Summary: Opportunity to join a newly formed, globally distributed team building an AI-powered internal developer platform for a large-scale engineering organization. The role focuses on designing and integrating LLM-based tools and agent systems that enhance coding, testing, and deployment workflows. You will help architect and implement core AI infrastructure and integrations within a greenfield, startup-like environment. 🗂️ Requirements: Hands-on experience integrating or building with LLMs, AI agents, or coding assistants, Strong experience in Scala or another modern JVM language (Kotlin or Java 17+), Proven polyglot programming ability, Experience designing and implementing API integrations, Ability to collaborate in a distributed team across time zones, Professional English communication skills 📃 Skills: Python, TypeScript, Scala, Java, Kotlin, LLM, OpenAI, Anthropic, API, AWS, GRPC, MySQL, GitHub, Slack, Docker, Kubernetes, GCP, CI/CD 🏢 Description: We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering our team. As part of the expanding VirtusLab Group, we offer a compelling environment for those seeking to make a substantial impact in the software industry within a forward-thinking organization. About the role You will be a founding member of a new, globally distributed team dedicated to a single mission: revolutionizing internal developer experience in a big technical organization (thousands of developers) via AI-based developer tools. Project scope You will be a founding member of a new, globally distributed team dedicated to a single mission: revolutionizing internal developer experience in a big technical organization (thousands of developers) via AI-based developer tools. Think of it as an internal startup, with the freedom to innovate and the stability of an established company. Our goal is to design and build an intelligent platform that assists developers at every stage, from coding and debugging to testing and deployment. Your team will be part of a larger 10-person group, with colleagues in California, Amsterdam, and Poland, organised into three focused sub-teams. This role requires collaboration (few meetings a week) with engineers on the US West Coast to ensure our platform meets the needs of our global team. What you will do Design and implement integrations with cutting-edge Large Language Models (LLMs) and APIs (like OpenAI’s models, Anthropic’s Claude, and more). Develop intelligent, agent-based systems to automate and assist in complex software development tasks. Engineer the core infrastructure for our AI agents, including components like MCP servers. Seamlessly weave AI capabilities into the daily workflows of our developers by integrating with essential tools like GitHub, Slack, IDEs, and client internal services, mostly Scala-based. Collaborate within a distributed, international team to research, prototype, and deploy solutions that have a direct impact on developer productivity and satisfaction. Tech stack This is a greenfield project, which means you’ll have a say in the technologies we choose. Nothing is set in stone. However, our environment and the problem space suggest we’ll be working with: Primary Languages: Python, TypeScript, Scala – based on available APIs and existing integration – but actual stack will be determined as the project progresses. The project will be based on internally developed services and solutions, utilising Scala, Java, AWS, GRPC, MySQL and more. What we expect: Proven polyglot programming skills with the ability to rapidly learn new languages, frameworks, and domains. Hands-on, practical experience building with or integrating LLMs, coding assistants, or AI agents. You should be ready to demonstrate your work or discuss your implementation experience during the interview. Strong, hands-on experience in at least one of the following: Scala, or another modern JVM language (e.g., Kotlin, Java 17+). A proactive, self-starter attitude. You take initiative, communicate clearly, and thrive in a collaborative, goal-oriented environment. Excellent communication skills in English and the ability to effectively collaborate with team members in the US West Coast time zone. Nice to have: A passion for or prior experience in building developer tools, IDE plugins, or enhancing developer workflows. Experience with DevOps practices and tools (CI/CD, Docker, Kubernetes, cloud platforms like GCP or AWS). Familiarity with the MLOps or Data Engineering ecosystem, particularly on the integration and tooling side (we are not looking for model researchers or trainers) A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package And a lot more!

🏢 Summary: Founding engineer role in a greenfield, AI-driven internal platform aimed at revolutionizing developer experience at scale through LLM-powered tools and agent-based systems. The position focuses on designing and integrating AI capabilities into daily development workflows across a large, globally distributed organization. You will collaborate internationally to build core infrastructure and intelligent automation supporting coding, testing, and deployment processes. 🗂️ Requirements: Hands-on experience building or integrating LLMs, AI agents, or coding assistants, Strong experience in Scala or another modern JVM language, Proven polyglot programming ability, Experience designing and implementing API integrations, Ability to build and maintain backend infrastructure, Fluent English for technical collaboration, Availability for collaboration with US West Coast time zone 📃 Skills: Python, TypeScript, Scala, Java, Kotlin, LLM, OpenAI, Anthropic, API, AWS, GRPC, MySQL, GitHub, Slack, Docker, Kubernetes, GCP, CI/CD 🏢 Description: We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering our team. As part of the expanding VirtusLab Group, we offer a compelling environment for those seeking to make a substantial impact in the software industry within a forward-thinking organization. About the role You will be a founding member of a new, globally distributed team dedicated to a single mission: revolutionizing internal developer experience in a big technical organization (thousands of developers) via AI-based developer tools. Project scope You will be a founding member of a new, globally distributed team dedicated to a single mission: revolutionizing internal developer experience in a big technical organization (thousands of developers) via AI-based developer tools. Think of it as an internal startup, with the freedom to innovate and the stability of an established company. Our goal is to design and build an intelligent platform that assists developers at every stage, from coding and debugging to testing and deployment. Your team will be part of a larger 10-person group, with colleagues in California, Amsterdam, and Poland, organised into three focused sub-teams. This role requires collaboration (few meetings a week) with engineers on the US West Coast to ensure our platform meets the needs of our global team. What you will do Design and implement integrations with cutting-edge Large Language Models (LLMs) and APIs (like OpenAI’s models, Anthropic’s Claude, and more). Develop intelligent, agent-based systems to automate and assist in complex software development tasks. Engineer the core infrastructure for our AI agents, including components like MCP servers. Seamlessly weave AI capabilities into the daily workflows of our developers by integrating with essential tools like GitHub, Slack, IDEs, and client internal services, mostly Scala-based. Collaborate within a distributed, international team to research, prototype, and deploy solutions that have a direct impact on developer productivity and satisfaction. Tech stack This is a greenfield project, which means you’ll have a say in the technologies we choose. Nothing is set in stone. However, our environment and the problem space suggest we’ll be working with: Primary Languages: Python, TypeScript, Scala – based on available APIs and existing integration – but actual stack will be determined as the project progresses. The project will be based on internally developed services and solutions, utilising Scala, Java, AWS, GRPC, MySQL and more. What we expect: Proven polyglot programming skills with the ability to rapidly learn new languages, frameworks, and domains. Hands-on, practical experience building with or integrating LLMs, coding assistants, or AI agents. You should be ready to demonstrate your work or discuss your implementation experience during the interview. Strong, hands-on experience in at least one of the following: Scala, or another modern JVM language (e.g., Kotlin, Java 17+). A proactive, self-starter attitude. You take initiative, communicate clearly, and thrive in a collaborative, goal-oriented environment. Excellent communication skills in English and the ability to effectively collaborate with team members in the US West Coast time zone. Nice to have: A passion for or prior experience in building developer tools, IDE plugins, or enhancing developer workflows. Experience with DevOps practices and tools (CI/CD, Docker, Kubernetes, cloud platforms like GCP or AWS). Familiarity with the MLOps or Data Engineering ecosystem, particularly on the integration and tooling side (we are not looking for model researchers or trainers) A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package And a lot more!