AI Engineer

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 & 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 3–4 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.). What We Offer Opportunity to work on cutting-edge Generative AI and Agentic AI initiatives at scale. Collaborative team environment with mentorship from senior AI leaders.

Continuous learning culture with access to the latest GenAI tools, models, and platforms. Competitive compensation and benefits.