Gen AI - Senior Developer
Role Overview
We’re looking for a GenAI senior Developer 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 usingframeworks 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 DevelopmentKit) 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.
