Key Responsibilities :

- Design, develop, and deploy Generative AI and LLM-based applications.

- Build and optimize RAG pipelines for enterprise AI use cases.

- Work with LLMs, prompt engineering, embeddings, and vector search technologies.

- Develop AI/ML solutions using Python and relevant frameworks.

- Design knowledge graphs and graph-based solutions using Neo4j.

- Integrate LLMs with enterprise applications, APIs, databases, and external services.

- Evaluate model performance, response quality, accuracy, and relevance.

- Implement techniques to improve LLM responses, including prompt optimization and retrieval strategies.

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

- Follow best practices for AI security, scalability, monitoring, and responsible AI development.

Required Skills & Experience :

- Strong hands-on experience in Generative AI / AI/ML.

- Experience working with Large Language Models (LLMs).

- Strong understanding of RAG architecture and implementation.

- Proficiency in Python and AI/ML frameworks.

- Hands-on experience with Neo4j or other graph databases.

- Experience with embeddings, vector databases, semantic search, and knowledge retrieval.

- Understanding of prompt engineering and LLM evaluation techniques.

- Experience integrating AI solutions through REST APIs and cloud platforms.

- Strong problem-solving and analytical skills.

Good to Have :

- Experience with LangChain, LlamaIndex, or similar GenAI frameworks.

- Exposure to OpenAI, Azure OpenAI, Gemini, Claude, or other foundation models.

- Experience with vector databases such as Pinecone, FAISS, Weaviate, or Milvus.

- Knowledge of cloud platforms such as AWS, Azure, or GCP.

- Experience with AI agents and agentic workflows.

- Exposure to MLOps, CI/CD, Docker, and Kubernetes.