GenAI Lead

Role Overview:

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.

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