Role Overview:

Were looking for a GenAI Developer to design, build, and deploy intelligent LLM-powered systemsfrom single-agent chatbots, copilots to complex multi-agent applicationsat 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.