Mandatory Skills :

- LLM (5-8 Year)

- Agentic AI (5-8 Year)

- Neo4j (5-8 Year)

Role/Job Description :

Key Responsibilities :

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

- Agentic AI Development : Build autonomous and multi-agent AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel; implement tool-use, planning, memory, and orchestration patterns.

- Knowledge Graphs : Model, build, and query knowledge graphs using Neo4j and other Graph Databases; integrate graph-based retrieval (GraphRAG) with LLM pipelines for enhanced reasoning and explainability.

- LLM Integration : Integrate and fine-tune Large Language Models (LLMs) using prompt engineering, function calling, structured outputs, and parameter-efficient techniques (LoRA/QLoRA) where applicable.

- Deployment & MLOps : Containerize and deploy GenAI services on AWS, Azure, or GCP; implement monitoring, evaluation, versioning, and cost-efficient scaling for AI workloads.

- Responsible AI : Apply guardrails to mitigate hallucinations, prompt injection, bias, and data leakage; contribute to evaluation frameworks for model accuracy and safety.

- Collaboration : Partner with cross-functional teams, document technical designs clearly, and communicate trade-offs effectively with both technical and non-technical stakeholders.

Required Technical Skills :

- Generative AI : Strong hands-on experience building GenAI applications using LLMs (OpenAI GPT, Anthropic Claude, Llama, Mistral, Gemini, etc.); solid grasp of Transformer architectures, embeddings, and prompt engineering.

- RAG : Proven experience designing RAG pipelines chunking, embeddings, vector databases (Pinecone, Chroma, Weaviate, Milvus, FAISS, pgvector), hybrid search, and re-ranking.

- Agentic AI & Tools : Hands-on experience with Agentic AI frameworks and tools such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, LlamaIndex, or similar; familiarity with MCP and function/tool calling patterns.

- Neo4j & Graph Databases : Practical experience with Neo4j (Cypher query language), graph data modeling, and integrating Graph DBs into AI/LLM workflows (GraphRAG is a strong plus).

- Programming : Strong Python skills; experience with frameworks such as PyTorch, TensorFlow, FastAPI, or similar; familiarity with REST APIs and async patterns.

- Cloud & Infrastructure : Working knowledge of at least one major cloud platform AWS (Bedrock, SageMaker), Azure (Azure OpenAI, AI Foundry), or GCP (Vertex AI); comfortable with Docker, Git, and CI/CD pipelines.

- Data Handling : Comfort working with structured and unstructured data, ETL processes, and SQL/NoSQL databases.

Experience & Qualifications :

- Experience : Preferably 5-6 years of overall software/AI engineering experience, with meaningful hands-on exposure to Generative AI projects.

- Education : Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

- Communication : Good written and verbal communication skills; able to explain complex AI concepts clearly to both technical and non-technical audiences.

- Problem-Solving : Strong analytical and debugging skills with a product-oriented mindset and a passion for delivering measurable business outcomes.

- Ownership : Self-driven, collaborative, and able to own features end-to-end from design through deployment.