Key Responsibilities :

- Design, build, and deploy GenAI-powered applications using LLMs, RAG pipelines, and agentic workflows.

- Architect and implement agent-based systems using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, etc.

- Develop multi-agent workflows for planning, tool usage, orchestration, memory handling, and task execution.

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like Qdrant, LlamaIndex, PGVector, etc.

- Implement real-time AI systems using FastAPI, Kafka, WebSockets, Redis, and streaming architectures.

- Integrate multiple data sources (PDFs, Docs, PPTs, Excel, databases) into intelligent document pipelines using tools such as LlamaParse and custom ingestion pipelines.

Develop AI-powered features such as :

1. Conversational bots

2. CV scoring systems

3. JD generators

4. Research automation tools

5. Enterprise chatbots

- Deploy and manage AI services on cloud platforms (AWS / Azure) using Dockerized services.

- Collaborate closely with product, engineering, and business stakeholders to translate requirements into scalable AI solutions.

- Contribute to building internal AI frameworks, accelerators, and reusable components.

Required Skills & Experience :

- 3-5 years of experience in Data Science / AI / ML, with strong recent focus on Generative AI systems.

Strong hands-on experience with :

- Python

- LLMs (OpenAI, GPT, Claude, Llama, etc.)

- Prompt Engineering

- Agentic AI frameworks (LangChain, LangGraph, AutoGen, CrewAI)

- Proven experience building RAG pipelines using:

- Vector databases (Qdrant, LlamaIndex, PGVector, etc.)

- Hybrid search (SQL + vector search)

- Experience with backend frameworks such as FastAPI for building AI APIs.

- Experience working with event-driven / real-time systems:

- Kafka

- WebSockets

- Streaming pipelines

- Experience with cloud deployments on AWS and/or Azure.

- Strong understanding of:

- LLM architecture

- Context management & memory

- Token optimization

- Latency optimization

- Evaluation of GenAI systems

- Solid experience working with SQL databases and data pipelines.

- Strong problem-solving, ownership mindset, and ability to work in fast-moving product environments.

Nice to Have :

Experience with :

- LLMOps / MLOps tools

- Monitoring & observability of GenAI systems

- CI/CD for ML systems

- Experience hosting or working with open-source LLMs.

- Understanding of security in GenAI systems (prompt injection, data privacy, access control).

- Experience building multi-tenant AI platforms or SaaS products.

- Exposure to Microsoft Teams / Slack / WhatsApp bot integrations