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.