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 & DeploymentDevelop 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 APIsUse Pydantic, dependency injection, and modular coding practices for maintainability.Proficient in working with databases using ORMs like SQLAlchemyEnsure 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 InterfacesDesign 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 & OrchestrationCreate 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 memoryMonitor 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 3.2/4Databases: BigQuery, Redis, FAISS, Pinecone, SQLAlchemy, Chroma, GCP Vector searchProtocols/APIs: REST, gRPC, WebSockets, OAuth2, OpenAPI, MCP, A2A Additional Good to have Tech Stack:DevOps: Docker, GitHub Actions, Jenkins, GKE, Cloud RunInfra & Tools: GCP, Azure, Pub/Sub,Artifact Registry, NGINX, Langfuse, Postman, PytestYears of Experience: Overall Experience: 10+ years of experience (with 2+ years in GenAI)Location: Bangalore.