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Mid-level role, metro location and 5+ years experience create moderate applicant competition.
Highly domain-specific LLM, agentic AI, vector DB and MLOps expertise limits cross-industry transferability.
Multiple mandatory requirements including 5+ years, production LLM experience, Azure and vector DB expertise enforce strict shortlisting.
Build and deploy production-grade Generative AI and agentic AI applications automating enterprise workflows from design through deployment.
Design and implement RAG pipelines including chunking, hybrid search, re-ranking, memory, and tool orchestration.
Deploy and monitor AI workloads on Azure with CI/CD, implement responsible AI guardrails, and define evaluation metrics for task success, hallucination, latency, and cost.
5+ years of ML/AI engineering experience with production LLM/agentic delivery.
Strong hands-on skills with Python, agent frameworks (LangGraph, AutoGen, CrewAI, or PydanticAI), and prompt engineering (GPT, Claude, LLaMA).
Experience with Azure AI stack, Databricks ML (MLflow, Delta Lake), vector databases (FAISS, Pinecone, Chroma), containerized deployments (Docker/Kubernetes, AKS/ARO), and CI/CD tools (Jenkins, GitHub Actions).
Portfolio of 3+ production AI deployments demonstrating measurable business impact.
Proven ability to architect and deliver complex AI systems involving RAG pipelines and agentic AI in enterprise settings.
Experience working with Azure cloud AI services and container orchestration platforms with strong CI/CD discipline.
Familiarity with deployment and monitoring of AI workloads including implementing AI guardrails and metrics to measure production effectiveness.