Role Summary :

Were looking for a GenAI Engineer to build practical AI features for our Data & AI team. Youll work across Python services, large language models, retrieval workflows, prompt design, vector search, and agent-based integrations.

What youll do :

- Build GenAI applications using Python and established LLM and GenAI frameworks.

- Design retrieval-augmented generation (RAG) workflows, including document processing, chunking, embeddings, retrieval, and response generation.

- Develop and test prompts, system instructions, evaluation sets, and guardrails for business use cases.

- Work with vector databases and hybrid search approaches to improve grounding and response quality.

- Build agents that can call tools, use APIs, follow permissions, and complete defined tasks safely.

- Create API services and application components that connect GenAI workflows to product interfaces.

- Measure response quality, latency, cost, and failure cases, then improve the system through testing and iteration.

What youll bring :

- 45 years of relevant software, data, machine learning, or AI engineering experience.

- Strong Python skills and experience building production services or data workflows.

- Hands-on experience with LLMs, prompt engineering, RAG, embeddings, and vector databases.

- Experience with agent patterns, tool calling, function calling, or workflow orchestration.

- Working knowledge of GenAI frameworks such as LangChain, LlamaIndex, Semantic Kernel, or comparable tools.

- Ability to evaluate model outputs, handle failure cases, and communicate trade-offs clearly.

- Understanding of REST APIs, Docker, Git, and software testing practices.

Good to have :

- Experience with Azure OpenAI, AWS Bedrock, Hugging Face, or similar platforms.

- Exposure to data privacy, prompt injection risks, access control, and enterprise AI governance.