ML Engineer

ML Engineer responsible for collaborating with Data Scientists to validate and scale ML and GenAI solutions, designing and operating Generative AI/LLM applications (including RAG pipelines and fine-tuned models), implementing MLOps for CI/CD and model lifecycle management, building scalable data infrastructure with data engineers, designing software architecture for maintainable code, developing analytical/ML pipelines with automated training and deployment workflows, implementing monitoring for models in production (drift, performance, hallucination), supporting responsible AI practices and regulatory compliance (e.g., EU AI Act), contributing to data quality frameworks, and working in cross-functional agile teams to deliver on-time AI solutions; required skills span Python (mandatory), ML/DL frameworks (PyTorch, TensorFlow), data/ML stack (SQL, Spark, Pandas, NumPy, Scikit-learn), MLOps tooling (MLflow, CI/CD, Docker/Kubernetes), GenAI/LLM ecosystems (HuggingFace, LangChain, vector databases), and experience with large datasets and scalable ML deployments.