About the role
Define architecture and technical strategy for GenAI platforms and solutions Lead design and implementation of complex RAG systems and agentic workflows Drive adoption of LangChain / LangGraph and advanced LLM orchestration frameworks Architect high-performance vector search and retrieval systems Oversee deployment strategies, including scalable cloud-native architectures Ensure reliability, observability, and governance of AI systems in production Lead and mentor engineering teams; conduct design reviews and code reviews Collaborate with stakeholders to align AI solutions with business goals Evaluate and integrate new tools, models, and frameworks in the GenAI ecosystem Expert-level proficiency in Python Extensive experience with RAG, LLMs, and prompt engineering at scale Strong expertise in LangChain / LangGraph and agent-based architectures Deep experience with Vector Databases and retrieval optimization Proven track record of deploying production-grade GenAI applications Strong experience with cloud (AWS/GCP/Azure), Kubernetes, and microservices architecture Solid understanding of system design, scalability, and distributed systems Experience leading GenAI/AI transformation initiatives Strong knowledge of LLMOps, MLOps, and governance frameworks Exposure to fine-tuning, evaluation frameworks, and guardrails Prior experience in client-facing or consulting roles Must have worked on at least one productionized AI/GenAI application Should have been involved in the end-to-end lifecycle: Problem definition → Data → Model → RAG → Deployment → Monitoring Strong ownership mindset and ability to work in fast-paced environments