About the role
Design and implement scalable GenAI and RAG-based architectures Lead development using LangChain / LangGraph and advanced orchestration techniques Architect and optimize vector search systems and embedding pipelines Develop and deploy APIs and microservices for GenAI applications Take ownership of end-to-end lifecycle: design, development, testing, deployment, monitoring Optimize performance, latency, and cost of LLM-based systems Mentor junior engineers and contribute to best practices Collaborate with product and business teams to translate requirements into AI solutions Strong expertise in Python and backend development Deep understanding of RAG pipelines and LLM architectures Hands-on experience with LangChain / LangGraph Strong experience with Vector Databases (Pinecone, Weaviate, FAISS, etc.) Proven experience deploying applications on cloud platforms (AWS/GCP/Azure) Experience with Docker, Kubernetes, CI/CD pipelines Solid understanding of system design and scalability Experience building enterprise-grade GenAI applications in production Familiarity with LLMOps / MLOps practices Exposure to multi-agent systems Experience with monitoring tools and observability