We are looking for a hands-on Generative AI Engineer to design, build, and deploy production-grade GenAI solutions. In this role, you will develop Retrieval-Augmented Generation (RAG) pipelines, build Agentic AI workflows using modern frameworks, and leverage graph databases such as Neo4j to power knowledge-grounded reasoning. You will collaborate closely with senior engineers, data scientists, and product teams to translate business problems into scalable, reliable AI systems.

- RAG Pipelines: Design and implement end-to-end Retrieval-Augmented Generation systems including chunking strategies, embedding models, vector stores, hybrid search, and re-ranking.

- Agentic AI Development: Build autonomous and multi-agent AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel.

- Knowledge Graphs: Model and query knowledge graphs using Neo4j and integrate GraphRAG with LLM pipelines.

- LLM Integration: Integrate and fine-tune LLMs using prompt engineering and structured outputs.

- Deployment & MLOps: Deploy GenAI services on AWS, Azure, or GCP with monitoring and scaling.

- Responsible AI: Apply guardrails to mitigate hallucinations, bias, and data leakage.

- Collaboration: Work with cross-functional teams and communicate technical insights.

Required Technical Skills

- Generative AI and LLMs (OpenAI, Claude, Llama, etc.)

- RAG pipelines and vector databases

- Agentic AI tools (LangChain, AutoGen, etc.)

- Neo4j and graph databases

- Python, APIs, PyTorch/TensorFlow

- Cloud platforms (AWS, Azure, GCP), Docker, CI/CD

Experience & Qualifications

- 3–4 years AI/software experience

- Degree in Computer Science or related field

- Strong communication and problem-solving skills

Nice-to-Haves

- GraphRAG experience

- Fine-tuning (LoRA/QLoRA)

- Multi-modal AI

- Certifications (AWS, Azure, GCP)