Job Description :

The ideal candidate is a self-motivated, multi-tasker, and demonstrated team-player.

You will be a lead developer responsible for the development of new software products and enhancements to existing products.

You should excel in working with large-scale applications and frameworks and have outstanding communication and leadership skills.

Responsibilities :

- Build scalable and production grade AI systems involving big data queries.

- Build MCP based Agentic AI chat bot dealing with intent classification, agent state management, etc.

- Build scalable REST APIs in FastAPI to integrate with LLM APIs (OpenAI, Claude, Azure OpenAI, etc.)

- Manage and query structured/unstructured data in MongoDB.

- Work closely with AI engineers to productionize GenAI use cases (chatbots, summarization, classification, embedding search).

- Design token-efficient API interactions and manage rate limits with LLM providers.

- Optimize performance, latency, and reliability of AI-enhanced APIs.

- Maintain clean, secure, and testable code across backend and frontend.

- Own end-to-end features: from UX to backend logic to API integration.

Qualifications :

Must-Have Skills :

- 7+ years of experience in full stack development.

- Strong hands-on experience with FastAPI (or Flask/Django) and Python.

- Strong hands-on experience with LangChain, LangGraph, Agentic AI and openAI LLM.

- Strong hands-on experience with Multi Agentic AI systems.

- Strong hands-on experience with MCP based AI architectures.

- Solid experience with MongoDB (including schema design and aggregation pipelines).

- Experience integrating with LLM APIs (e.g., OpenAI, Anthropic, Cohere, Mistral, Azure OpenAI, etc.)

- Deep understanding of RESTful API design and best practices.

- Git, Docker, and familiarity with CI/CD pipelines.

Additional Information :

Nice to Have :

- Familiarity with prompt engineering, embeddings, vector databases (like Pinecone, FAISS, Weaviate).

- UI/ Angular knowledge.

- Experience working on GenAI-driven UIs (chat interfaces, knowledge panels, QCA).

- Knowledge of JWT, OAuth2, API rate limiting strategies.

- Basic understanding of LLM token usage, context length constraints, and caching.

- Experience with PostgreSQL or hybrid Mongo/Postgres data models.

- DevOps awareness: Kubernetes, cloud deployment (AWS/Azure/GCP).