DevOps/MLOps Engineer with AI, REMOTE WARSZAWA
- Minimum of 5+ years of hands-on experience in DevOps/MLOps
- Being able to work until at least 7:00 PM CET
- Proficiency with at least one cloud (preferably Azure or AWS)
- Hands-on experience in self-hosting LLMs and building infrastructure for model evaluations
- Solid knowledge of Kubernetes, Terraform, and Helm
- Some experience with the open-source MLOps ecosystem,like model serving, model routing, vector databases/RAG, and experiment tracking tools (e.g. vLLM, LiteLLM, Qdrant, MLflow, Weights & Biases, or equivalent).
Hi there! If you’re looking for a high-impact position in an ambitious software house, we’ve got a match for you!
Currently, we are searching for a full-time MLOps/DevOps Engineer to cooperate with our US client - the company integrates production-grade, governed AI workflows into complex enterprise systems by addressing common friction points like poor integration and lack of domain expertise. The organization combines deep domain knowledge with cutting-edge capabilities to accelerate the deployment of reliable AI products.
Our client’s core mission is to make artificial intelligence work for complex enterprise workflows. Recognizing that most AI initiatives fail due to poor integration, gaps in domain specialization, a lack of governance, and unclear ownership-rather than flawed models—they focus on overcoming these exact friction points.
By combining decades of deep industry domain expertise with cutting-edge product AI capabilities, they accelerate production deployment and embed production-grade, governed AI workflows directly into some of the world’s most intricate enterprise systems. The leadership team consists of proven industry veterans with a track record of delivering groundbreaking AI products, backed by a board of senior executives with deep expertise in global consulting and software engineering.
⚠️ Due to the client's location, a daily standup around 5:00 – 6:00 PM Polish time, with required availability to work until at least 7:00 PM CET.
,[Design, implement, and optimize open-source infrastructure for model routing, knowledge bases, memory systems, and model customization., Deploy and maintainopen-source models for inference, ensuring scalability, reliability, and performance., Optimize infrastructure for efficient model training and inference workloads across cloud and containerized environments., Build and maintain model evaluation frameworks and benchmarking pipelines using open-source tools., Manage data pipelines and datasets to support AI model development, experimentation, evaluation, and continuous improvement.] Requirements: DevOps, MLOps, Cloud, Azure, AWS, LLM, Kubernetes, Terraform, Helm, MLOps Ecosystem, vLLM, LiteLLM, Qdrant, MLflow Tools: . Additionally: Sport subscription, Private healthcare, Flat structure, Small teams, International projects.