Generative AI Engineer

**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: Bachelor’s or Master’s 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. Pay: ₹135,000\.00 \- ₹140,000\.00 per month Work Location: In person Note: This is a third party job (Aggregated by careeruplift.ai). Shortlisting and Final hiring decision & process is handled by the company.