Candidate must have a minimum 5-7 years' experience in Machine Learning and the past 2 years in Generative AI. Total Min Experience after Graduation must be 13-15 years

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 :

- 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