Machine Learning Engineer-Agentic AI

ML Engineer – Agentic AI Responsibilities Design and implement multi-agent systems using LangChain, LangGraph, CrewAI, AutoGen or similar frameworks. Build A2A (agent-to-agent) orchestration and implement MCP (multi-context protocol) for context reuse and collaboration. Fine-tune foundation models (LoRA, RLHF, DPO) and integrate retrieval-augmented generation (RAG) pipelines. Develop APIs and backend services (FastAPI/Flask/Node.js) and integrate with enterprise systems (HRIS, CRM, ERP). Build secure data pipelines (ETL, embeddings, vector DB ingestion) and ensure governance, bias mitigation, and lineage. Containerize, deploy, and monitor agents (Docker, Kubernetes, CI/CD pipelines) with observability and drift detection. Collaborate with QA, red teaming, and product leads to benchmark accuracy, safety, and compliance. Skills Required Strong in Python, PyTorch/TensorFlow, Hugging Face, and fine-tuning techniques (LoRA, RLHF). Hands-on with LangChain/LangGraph, MCP, A2A orchestration, and multi-agent architectures. Proficient in ETL pipelines, SQL/NoSQL, vector DBs (Pinecone, Weaviate, FAISS, Milvus). Backend expertise in FastAPI/Flask/Node.js, REST/GraphQL APIs, and microservices. DevOps/MLOps experience: Docker, Kubernetes, CI/CD (Jenkins/GitHub Actions), monitoring/observability. Cloud experience with AWS/GCP/Azure AI services and secure integration practices. Bonus: familiarity with enterprise HR/ATS systems (Workday, SAP, Paychex) and workflow automation.

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