AI Engineering Lead

AI Engineering LeadRole Overview

We are looking for a highly skilled AI Engineering Lead (Senior AI/ML Engineer) with hands-on expertise in designing, building, and deploying production-grade Generative AI and Agentic AI solutions. In this role, you will lead the architecture and implementation of autonomous AI agents, multi-agent systems, advanced RAG architectures, and scalable AI platform workflows across enterprise cloud ecosystems.

Key Details

• Role Title: AI Engineering Lead

• Experience Required: 7–10 Years

• Work Location: Noida (Hybrid)

Key Responsibilities

• Enterprise AI Deployment: Design, build, and deploy production-grade enterprise Generative AI and Agentic AI applications.

• Multi-Agent Systems: Architect and implement autonomous multi-agent systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, and MCP (Model Context Protocol).

• RAG Architecture: Develop and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging vector databases (Pinecone, ChromaDB, Weaviate, FAISS).

• LLM Integration: Integrate leading foundation models (OpenAI, Azure OpenAI, Gemini, Anthropic, AWS Bedrock) into core business products and enterprise workflows.

• LLMOps & Governance: Establish robust LLMOps practices, prompt engineering frameworks, model evaluation, guardrails, security controls (RBAC, prompt injection protection), and AI governance standards.

• API Development & Observability: Build high-performance REST APIs using FastAPI and implement full-stack AI observability using LangSmith, Langfuse, or OpenTelemetry.

• Performance & Cost Optimization: Optimize AI application performance for latency, token efficiency, throughput, scalability, and operational costs.

• Leadership & Strategy: Partner with cross-functional stakeholders to translate complex business problems into AI-driven solutions while mentoring junior engineers and driving AI best practices.

Technical Skills & QualificationsMandatory Skills:

• Programming & Backend: Python, FastAPI, SQL / PostgreSQL, Git & CI/CD

• AI & LLMs: Generative AI, LLM Integration, Advanced Prompt Engineering, RAG

• Agentic Frameworks: LangChain, LangGraph, CrewAI, Model Context Protocol (MCP)

• Vector Databases: Pinecone, ChromaDB, Weaviate, FAISS

• LLMOps & Observability: LangSmith, Langfuse, Agent Evaluation & Observability

• Cloud & AI Platforms: Azure (AI Services, OpenAI, ML), AWS (Bedrock, SageMaker), Google Vertex AI, Databricks

Preferred Skills:

• Graph & Security: Knowledge Graphs (Neo4j, GraphRAG) and NVIDIA NeMo Guardrails

• Agentic & Infrastructure: AutoGen, OpenTelemetry, Docker / Kubernetes

• ML Platforms & Frameworks: PyTorch / TensorFlow, MLflow / Kubeflow

• Document Intelligence: OCR & Intelligent Document Processing (IDP)