- Architect and implement RAG, semantic search, and knowledge retrieval solutions using vector databases.

- Build AI systems using LLMs, embeddings, prompt engineering, AI agents, and orchestration frameworks.

- Establish AI platform, MLOps, and LLMOps standards for model deployment, monitoring, and governance.

- Ensure AI security, responsible AI, data privacy, and regulatory compliance. Collaborate with business, data, engineering, and cloud teams to deliver AI-driven solutions.

Required Skills :

- Strong expertise in Generative AI, Agentic AI, RAG, GraphRAG, Vector Databases, Semantic Search, and Knowledge Management.

- Experience with AI frameworks such as LangChain, LangGraph, and Semantic Kernel.

- Hands-on experience with vector databases such as Pinecone, Weaviate, or Milvus.

- Experience with cloud AI platforms on Microsoft Azure.

- Strong knowledge of AI governance, data architecture, and enterprise integration patterns.

- Proficiency in Python, APIs, and scalable distributed systems.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.

- 8+ years of experience in solution architecture, data engineering, AI/ML, or cloud architecture, including hands-on experience with GenAI and enterprise AI implementations.