GenAI Architect

Role & responsibilities

• Over 3 years of 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.

• Strong knowledge of LLM security risksprompt injection, jailbreaking, data exfiltration, tool misuse—and 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.