Role Summary

We are looking for a hands-on Generative AI Engineer to design, build, and deploy productiongrade 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 & 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: Solid 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: 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.)

📌 Artificial Intelligence Engineer (Hyderabad)
🏢 cirruslabs
📍 Hyderabad