Genai Lead

GenAI Lead Engineer– Job Description Role Overview: We’re looking for a GenAI lead to design, build, and deploy intelligent LLM-powered systems—from single-agent chatbots, copilots to complex multi-agent applications—at scale. We are particularly interested in candidates who have hands-on experience in taking GenAI applications from concept to production, especially within high-volume B2C environments. This role prioritizes individuals who understand the nuances of deploying, maintaining, and optimizing GenAI solutions for real-world users, beyond the scope of Proof-of-Concept (PoC) development. You will work across the full stack, integrating LLMs, microservices, vector databases, backend APIs, and modern cloud infrastructure. Key Responsibilities: 1. GenAI Application Development & Deployment - Develop scalable, asynchronous microservices using Python (FastAPI) for chatbots, copilots, and agentic workflows. - Design event-driven architectures to support high concurrency, rate limiting, and real-time responsiveness. - Implement secure, versioned REST/gRPC APIs - Use Pydantic, dependency injection, and modular coding practices for maintainability. - Proficient in working with databases using ORMs like SQLAlchemy - Ensure observability using logging, metrics, tracing, and health checks. - Create responsive React.Js frontends integrated via REST APIs or WebSockets. - Deploy applications on Cloud Run, GKE, using Docker, Artifact registry, CI/CD pipelines 2. LLM-Powered Conversational Interfaces - Design and build LLM-powered chatbots, voicebots, copilots and other applications using LangChain or custom orchestration frameworks. - Integrate enterprise-grade LLM APIs (Gemini, OpenAI, Claude) for multi-turn, intelligent interactions. - Implement user session management and context/state tracking for personalized and continuous conversations. - Build RAG pipelines with vector databases, knowledge graphs to ground responses with external knowledge and documents. - Apply advanced prompt engineering (ReAct, Chain-of-Thought with tool calling) for precise and goal-oriented outputs. - Ensure performance in low-latency, streaming environments using WebSockets, gRPC, and SIP media gateways. - Perform fine-tuning of open-source LLMs (LLaMA variants) using techniques like SFT, LoRA, for cost-effective domain adaptation. - Optimize high-speed inference pipelines leveraging multi-GPU clusters (up to 8x H100s) to reduce latency and improve throughput. 3. Multi-Agent Systems & Orchestration - Create multi-agent systems & Implement orchestration patterns like supervisor-agent, hierarchical, and networked agents using frameworks like ADK, Pydantic AI and LangGraph. - Use LangGraph for stateful workflows with memory, conditional branching, retries, and async execution. - Enable persistent context and long-term memory - Monitor behavior, drift, and performance using observability tools. - Skilled in developing agents with ADK and A2A protocols & experienced in configuring custom and remote MCP servers. Preferred Tech Stack: - Languages/Frameworks: Python, FastAPI, HTML, CSS, React.Js, LangChain, LangGraph, Pydatic AI, ADK (Agent Development Kit) - LLMs & Agents: OpenAI (GPT-4), Claude, Gemini, Mistral, LLaMA 3.2/4 - Databases: BigQuery, Redis, FAISS, Pinecone, SQLAlchemy, Chroma, GCP Vector search - Protocols/APIs: REST, gRPC, WebSockets, OAuth2, OpenAPI, MCP, A2A Additional Good to have Tech Stack: - DevOps: Docker, GitHub Actions, Jenkins, GKE, Cloud Run - Infra & Tools: GCP, Azure, Pub/Sub,Artifact Registry, NGINX, Langfuse, Postman, Pytest Years of Experience: Overall Experience: 10+ years of experience (with 2+ years in GenAI) Location: Bangalore.