Genai lead

Gen AI Lead Engineer– Job Description Role Overview: We’re looking for a Gen AI 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 Gen AI applications from concept to production, especially within high-volume B2 C environments. This role prioritizes individuals who understand the nuances of deploying, maintaining, and optimizing Gen AI solutions for real-world users, beyond the scope of Proof-of-Concept (Po C) development. You will work across the full stack, integrating LLMs, microservices, vector databases, backend APIs, and modern cloud infrastructure. Key Responsibilities: 1. Gen AI Application Development & Deployment Develop scalable, asynchronous microservices using Python (Fast API) for chatbots, copilots, and agentic workflows. Design event-driven architectures to support high concurrency , rate limiting, and real-time responsiveness . Implement secure, versioned REST/g RPC 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 Web Sockets. 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 Lang Chain or custom orchestration frameworks. Integrate enterprise-grade LLM APIs (Gemini, Open AI, 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 (Re Act, Chain-of-Thought with tool calling) for precise and goal-oriented outputs. Ensure performance in low-latency, streaming environments using Web Sockets, g RPC, and SIP media gateways. Perform fine-tuning of open-source LLMs (LLa MA variants) using techniques like SFT, Lo RA, 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 Lang Graph. Use Lang Graph 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 A2 A protocols & experienced in configuring custom and remote MCP servers. Preferred Tech Stack: Languages/Frameworks: Python, Fast API, HTML, CSS, React.js, Lang Chain, Lang Graph, Pydatic AI, ADK (Agent Development Kit) LLMs & Agents: Open AI (GPT-4), Claude, Gemini, Mistral, LLa MA 3.2/4 Databases: Big Query, Redis, FAISS, Pinecone, SQLAlchemy, Chroma, GCP Vector search Protocols/APIs: REST, g RPC, Web Sockets, OAuth2, Open API, MCP, A2 A Additional Good to have Tech Stack: Dev Ops: Docker, Git Hub 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 Gen AI) Location: Bangalore.