About the role:
We are looking for an experienced AI/ML Engineer with strong software engineering expertise and hands-on experience in Generative AI, LLMs, agentic systems, and production AI applications.
You will be responsible for designing scalable AI architectures, developing multi-agent workflows, building production-grade RAG systems, integrating enterprise tools and data, and optimizing AI applications for performance and reliability.
Key Responsibilities
- Design and develop multi-agent AI systems with planning, state management, tool usage, and self-correction capabilities.
- Build agentic workflows using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration.
- Implement MCP (Model Context Protocol) servers/clients, tool calling, and API integrations with enterprise systems.
- Develop scalable and low-latency RAG pipelines using vector databases, hybrid search, semantic caching, and re-ranking.
- Implement LLM evaluation, observability, tracing, and guardrails using tools such as Ragas and Langfuse.
- Develop high-performance asynchronous Python microservices and optimize latency, context usage, and inference performance.
- Explore and implement local model serving, fine-tuning, and quantization where appropriate.
- Mentor junior engineers and contribute to technical architecture and engineering best practices.
Technical Skills
- Python: 4+ years of professional backend development; strong experience with FastAPI, asyncio, and microservice architectures.
- LLMs & Generative AI: Hands-on experience with OpenAI, Anthropic Claude, Gemini, Llama, Mistral and knowledge of fine-tuning/quantization.
- Agentic AI: Strong experience with LangGraph, AutoGen, CrewAI, or custom agent orchestration frameworks.
- RAG & Search: Experience with Pinecone, Weaviate, Qdrant, Milvus, hybrid search, semantic retrieval, and re-ranking.
- Databases: Good knowledge of relational and NoSQL databases and enterprise data integrations.
- MCP & Tool Integration: Experience building or integrating MCP servers/clients, tool calling, and APIs.
- Cloud & Deployment: Experience with Docker, Kubernetes, AWS/GCP/Azure, and CI/CD pipelines.
- LLMOps: Exposure to monitoring, tracing, evaluation, guardrails, hallucination mitigation, and production AI observability.
Pay: ₹300,000.00 - ₹500,000.00 per year
Education:
Experience:
- Python: 2 years (Preferred)
- AI: 2 years (Preferred)
Work Location: In person
