
GenAI Lead Engineer
2 days ago
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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