My Client are developing advanced AI systems that rely on large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI frameworks to deliver accurate, real-time insights. To achieve production-grade reliability, scalability, and seamless integration with existing platforms, we require an ML engineer with strong expertise in AI/ML methods, Python programming, and modern deployment practices. This role must bridge cutting-edge model development with robust service and API engineering, ensuring smooth integration, streaming capabilities, and optimized deployment. Experience with cloud platforms and vector databases, is desirable.

Must Have

• Hands-on experience with large language models (LLMs) and retrieval-augmented generation (RAG).
• Experience with agentic AI frameworks (e.g., LangChain, LangGraph).
• Strong background in core AI/ML techniques (e.g., supervised/unsupervised learning, deep learning, NLP).
• Proficient programming skills in Python, including experience with common ML/AI libraries (e.g., PyTorch, TensorFlow, scikit-learn).
• Solid understanding of service and API engineering, including real-time/streaming implementation and hardware/software compatibility checks.
• Experience with integration and release engineering: shipping new versions to production, integrating with other system components, and assessing impact on end-to-end performance.
• Hands-on experience in model deployment (e.g., containerization, orchestration with Docker/Kubernetes, inference optimization).

Nice to Have

• Experience with cloud platforms (Azure), including AI/ML services.
• Knowledge of vector databases for retrieval applications.
• Familiarity with observability and monitoring for deployed ML/AI systems (logging, tracing, metrics).