Senior AI ML Engineer

Must Have Technical/Functional Skills
Databricks and Azure AI Foundry must-have; ML engineering/Applied AI; production ML deployment; RAG and Agentic AI systems; Python; model serving frameworks and API development such as FastAPI/MLflow; vector databases and embeddings; LangChain/LlamaIndex or similar orchestration; CI/CD; containerization; cloud deployment; predictive ML and agentic evaluation; MLOps/model monitoring preferred; regulated/high-compliance environments preferred.

Roles & Responsibilities
Design, build, and deploy AI/ML solutions; lead full AI lifecycle from experimentation to production; build ML models and LLM-powered RAG/agent systems; translate business challenges into AI solutions and KPIs; design embeddings/vector search and orchestration patterns; own end-to-end implementation, deployment, and operations; evaluate performance with automated and human-in-the-loop methods; optimize latency, cost, scalability, reliability; implement CI/CD, MLOps, monitoring, drift detection, retraining, lifecycle management; mentor and promote responsible AI governance.
Generic Managerial Skills, If any
Autonomous ownership; mentoring; architecture influence; curiosity and emerging-AI mindset; cross-functional collaboration.