Total Experience : 8 Years
Location : Hyderabad
Job Description :
Role Summary
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.
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 s 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 s 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 s 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 s 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 s Qualifications
- Experience: 8-12 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.
Nice-to-Haves
- Experience with GraphRAG or hybrid graph + vector retrieval architectures.
- Exposure to fine-tuning LLMs/SLMs using LoRA/QLoRA or instruction tuning.
- Experience with multi-modal AI (text + image / video / audio).
- Contributions to open-source GenAI projects or relevant publications.
- Certifications such as AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer Associate, or Google Cloud Professional ML Engineer.
- Familiarity with LLM observability and evaluation tools (LangSmith, Langfuse, Ragas, TruLens, etc.).
