Role & Responsibilities :

- Design and define enterprise AI, Generative AI, and Agentic AI solution architectures.

- Architect and implement end-to-end AI solutions using Large Language Models (LLMs), RAG, and semantic search.

- Develop AI applications using frameworks such as LangChain, LangGraph, or Semantic Kernel.

- Design and implement enterprise knowledge management solutions using vector databases and document repositories.

- Build scalable, secure, and cloud-native AI solutions on Microsoft Azure.

- Collaborate with business stakeholders, data engineers, developers, and cloud teams to deliver production-ready AI solutions.

- Establish AI architecture best practices, including MLOps, LLMOps, governance, security, and Responsible AI.

- Integrate AI solutions with enterprise applications, APIs, databases, and cloud services.

- Provide technical leadership, architecture guidance, and code reviews throughout the project lifecycle.

- Ensure AI solutions meet enterprise standards for performance, scalability, security, and compliance.

Preferred Candidate Profile :

- 8+ years of experience in Solution Architecture, Software Engineering, Cloud Architecture, AI/ML, or Data Engineering.

- Hands-on experience delivering enterprise Generative AI solutions using LLMs.

- Strong expertise in RAG, Agentic AI, semantic search, and enterprise knowledge management.

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

- Strong knowledge of Microsoft Azure services, including Azure OpenAI and Azure AI Search.

- Experience with vector databases such as Pinecone, Weaviate, Milvus, or Azure AI Search.

- Strong programming skills in Python and experience developing REST APIs.

- Good understanding of AI governance, Responsible AI, data privacy, and enterprise security.

- Experience working with distributed systems, microservices, and cloud-native architectures.

- Excellent communication, stakeholder management, and problem-solving skills.