GenAI Lead Engineer

23 hours ago

Bengaluru, Karnataka, India L&T Finance Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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 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

    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, , 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

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