Role & responsibilities
- Experience in developing and scaling Generative AI projects from prototypes to enterprise production, managing throughput, latency, cost, and multi-region deployments.
- Proven expertise implementing AI interoperability protocols like MCP (Model Context Protocol) and A2A (Agent-to Agent) at scale for seamless system integration.
- Skilled in using enterprise cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, and Google Vertex AI to build and deploy production-grade agentic AI solutions.
- Advanced Python programming skills and hands-on experience with agentic AI frameworks including LangChain, LangGraph, CrewAI, and AutoGen for building robust generative AI applications.
- Deep understanding of advanced Retrieval Augmented Generation (RAG)



architectures (Graph RAG, Vectorless RAG, Hybrid RAG) and traditional AI/ML fundamentals like model building, fine-tuning, and evaluation.
- Solid knowledge of LLM security risksprompt injection, jailbreaking, data exfiltration, tool misuseand experience designing defense-in-depth safeguards within agentic system architectures.
- Expertise in containerization and cloud-native orchestration (Kubernetes, Docker, serverless) and event-driven architectures for scalable deployment of agentic AI workloads; holds relevant AI or solution architecture certifications.

📌 Generative AI Architect (Bengaluru)
🏢 Pricewater House Coopers Service Delivery Center Bangalore
📍 Bengaluru