AI Lead Engineer

Role Overview - AI Lead Engineer (GenAI & Agentic AI)

Location - Pune / Hyderabad

Experience: 10-15+ Years (including 3-5+ years in AI/ML, Generative AI, or Agentic AI Solutions)

Role Overview

We are looking for an experienced AI Lead Engineer to drive the design, architecture, and delivery of enterprise-grade AI solutions leveraging Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. The ideal candidate will have a strong software engineering background combined with expertise in AI solution design, AI agent orchestration, cloud platforms, and modern MLOps practices.

Key Responsibilities

• Lead the design, architecture, and implementation of AI/GenAI solutions.
• Build and deploy scalable LLM-powered applications and RAG systems.
• Design and implement Agentic AI and Multi-Agent systems for business automation and intelligence.
• Define enterprise AI architecture, governance, security, and best practices.
• Collaborate with business stakeholders, product teams, and engineering teams to deliver AI solutions.
• Mentor development teams and drive AI innovation initiatives.
• Evaluate emerging AI technologies and recommend adoption strategies.

Technical Skills

• Strong expertise in AI Solution Architecture & Design.
• Strong programming skills in Python and Temporal.
• Experience with TensorFlow, PyTorch, Scikit-learn (preferred).
• Expertise in Generative AI, LLMs, RAG, Vector Databases, AI Agents, and Multi-Agent Systems.
• Hands-on experience with LangChain, LlamaIndex, Semantic Kernel, LangGraph, CrewAI, AutoGen, or similar frameworks.
• Strong understanding of Prompt Engineering, Fine-Tuning, Embeddings, and RAG Optimization.
• Experience with Azure OpenAI, OpenAI GPT Models, Claude, Gemini, Llama, Mistral, or equivalent foundation models.
• Knowledge of Model Context Protocol (MCP), Tool Calling, Function Calling, and Agent Orchestration.
• Experience with Pinecone, Qdrant, Weaviate, ChromaDB, FAISS, Azure AI Search, or similar vector databases.
• Experience with Azure AI Services, Azure OpenAI, AWS AI/ML Services, or Google Vertex AI.
• Strong understanding of MLOps/LLMOps practices using MLflow, Kubeflow, Databricks, Azure ML, etc.
• Experience with APIs, microservices, Docker, Kubernetes, and cloud-native architectures.
• Knowledge of SQL, NoSQL databases, and data engineering concepts.
• Experience with AI monitoring, observability, evaluation frameworks, and governance practices.

Preferred Qualifications

• Experience in GraphRAG, Knowledge Graphs, and Enterprise Search solutions.
• Exposure to AI governance, compliance, and Responsible AI practices.
• Experience leading AI transformation initiatives and customer-facing engagements.

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