Key Responsibilities
- Lead the end-to-end AI product lifecycle—ideation, prototyping, MVP, and enterprise-grade rollout
- Architect scalable enterprise AI applications using LLMs, embeddings, and agentic workflows
- Apply integration design patterns (API Gateway, event-driven, CQRS, pub-sub) for seamless enterprise system integration
- Design and implement LLM-powered applications (OpenAI, Gemini, Claude, LLaMA) with modular workflows
- Build and optimize embedding pipelines and semantic search/RAG pipelines with vector databases (FAISS, Weaviate, Pinecone, Milvus)
- Deploy AI workloads on Azure AI or Google Cloud AI with MLOps practices for automated training, CI/CD, and monitoring
Requirements
- 15+ years of IT experience with strong data architecture expertise
- 8+ years in AI/ML development, including 3+ years in enterprise LLM/agentic AI solutions
- Hands-on expertise with LangChain, LangGraph, and design patterns for workflow orchestration
- Deep understanding of agentic AI architectures, embeddings, RAG pipelines, and semantic search
- Proficiency with vector databases, MLOps pipelines, CI/CD, and Kubernetes