We are seeking a Lead AI Engineer with 7 8 years of experience to design and lead the development of enterprise-scale Generative AI platforms and Agentic AI systems.
The ideal candidate will have strong expertise in LLM-based application development, multi-agent systems, RAG architectures, and scalable AI infrastructure. This role will drive the design and implementation of AI copilots, enterprise knowledge assistants, automation agents, and AI-driven workflows that integrate with enterprise systems and data platforms.
You will lead the architecture and development of production-grade AI solutions, mentor AI engineers, and define best practices for building scalable, reliable, and secure AI applications.
Key Responsibilities
- Design and architect enterprise Generative AI platforms and AI-powered applications
- Lead the development of AI copilots, enterprise assistants, and agent-based automation systems
- Design multi-agent architectures using modern agent frameworks
- Architect and implement RAG (Retrieval-Augmented Generation) platforms
- Build scalable LangChain / LlamaIndex / LangGraph orchestration pipelines
- Develop AI agents capable of reasoning, planning, and executing tasks
- Design AI microservices and APIs using Python-based frameworks
- Develop interactive AI tools and dashboards using Streamlit
- Integrate LLM APIs such as OpenAI, Azure OpenAI, Claude, Gemini, and open-source models
- Design vector search and semantic retrieval architectures
- Optimize prompt engineering strategies and LLM inference pipelines
- Work closely with MLOps teams to deploy, monitor, and scale AI services
- Define AI architecture standards, best practices, and governance
- Mentor and guide AI engineers and cross-functional teams
- Evaluate and adopt emerging GenAI and AI agent technologies
Must-Have Skills
- Expert Python programming for AI and backend systems
- Extensive experience building LLM-powered applications
- Strong expertise in LangChain, LlamaIndex, or similar AI frameworks
- Experience implementing RAG architectures
- Experience developing Streamlit applications
- Experience integrating LLM APIs
- Strong understanding of AI system architecture
- Experience designing microservices and API-based AI systems
- Experience with vector databases and semantic search
- Experience with Git-based development workflows
Good-to-Have Skills
Agentic AI Frameworks : LangGraph, CrewAI, Microsoft AutoGen
AI / GenAI Tools : Haystack, LangSmith, Guardrails AI
Vector Databases : pgvector, Pinecone, Chroma, FAISS
