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Remote Machine Learning/AI Engineer @ DS STREAM
  • Warsaw
Remote Machine Learning/AI Engineer @ DS STREAM
Warszawa, Warsaw, Masovian Voivodeship, Polska
DS STREAM
8. 3. 2026
Informacje o stanowisku

We are seeking a skilled AI/ML Engineer to join our team and drive impactful data solutions. Youll handle core data engineering tasks like data clean-up, loading, and access control, while building and maintaining advanced ML models using Azure ML. This is an exciting opportunity to work on real-world projects with cutting-edge tools.

Ideal candidates have direct Azure ML experience, but strong performers from related fields will be considered. If youre passionate about scalable ML solutions and thrive in collaborative environments


  • Strong hands-on experience with Azure (essential).
  • Proficiency in Python.
  • Familiarity with StreamlitDashAzure MLKubernetes (K8s).
  • Experience with QA/unit testing and CI/CD (GHA).
  • Background in data engineering (clean-up, loading, access control) is a plus.

We are seeking a skilled AI/ML Engineer to join our team and drive impactful data solutions. Youll handle core data engineering tasks like data clean-up, loading, and access control, while building and maintaining advanced ML models using Azure ML. This is an exciting opportunity to work on real-world projects with cutting-edge tools.

Ideal candidates have direct Azure ML experience, but strong performers from related fields will be considered. If youre passionate about scalable ML solutions and thrive in collaborative environments

,[Perform data engineering tasks: clean-up, loading, and implementing access controls., Design, build, and maintain ML solutions on Azure ML., Develop user-friendly interfaces with Streamlit and Dash., Deploy and manage applications on Azure and Kubernetes (K8s)., Write comprehensive QA/unit tests and set up CI/CD pipelines using GitHub Actions (GHA). Requirements: Azure, Python, Streamlit, Dash, Azure ML, Kubernetes, K8s, QA, Unit testing, CI/CD, Data engineering Additionally: Sport subscription, Training budget, Flat structure, Private healthcare, Small teams, Free coffee, No dress code, Startup atmosphere.

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