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: - Pune

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