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.