Senior AI Engineer

About the role

We are looking for a senior AI engineer to own the AI layer of our data platform—building production-grade LLM applications, retrieval systems, intelligent agents, and natural-language interfaces over enterprise data.

You will work across RAG, embeddings, vector and hybrid search, agent/tool-calling architectures, LLM evaluation, and self-hosted open-weight models.

This is a hands-on engineering role for someone who has moved beyond prototypes and has built, deployed, and operated LLM systems in production.

What you’ll own

Design and build production-grade LLM applications and RAG systems.

Own retrieval architecture including chunking, embeddings, vector search, hybrid search, and reranking.

Build agentic and tool-calling systems with appropriate permissions, scoping, validation, and guardrails.

Develop natural-language interfaces over enterprise data and structured databases.

Build and maintain LLM evaluation frameworks, including test sets, regression suites, grounding, hallucination, and answer-quality evaluation.

Work within our data platform and engineering stack rather than relying solely on hosted AI APIs.

Deploy and optimize self-hosted open-weight models using technologies such as vLLM or equivalent serving infrastructure.

Optimize inference performance, GPU utilization, latency, throughput, and cost.

Explore and implement fine-tuning or model adaptation when appropriate.

Collaborate with data and software engineers to turn AI capabilities into reliable production products.

Required qualifications

5+ years of software or data engineering experience.

At least 2 years of hands-on experience building and deploying production LLM-based systems.

Strong Python engineering skills.

Deep understanding of RAG and retrieval architecture:

Chunking strategies

Embeddings

Vector databases/search

Hybrid search

Reranking

Retrieval evaluation

Experience building LLM agents or tool-calling systems.

Understanding of permissions, access control, scoping, validation, and guardrails for AI systems.

Strong understanding of LLM evaluation, including test datasets, regression testing, grounding, and hallucination detection.

Experience working directly with data platforms, databases, or enterprise data, rather than only consuming hosted LLM APIs.

Strong software engineering fundamentals and experience taking systems from prototype to production.

Strongly preferred

Experience with self-hosted open-weight models.

Production experience with vLLM or equivalent model-serving infrastructure.

Understanding of GPU resource management and inference optimization.

Experience with fine-tuning, LoRA, or other model-adaptation techniques.

Experience with Text-to-SQL systems.

Experience designing or using a semantic layer over real enterprise data models.

Experience combining unstructured documents with structured enterprise data in a single AI application.

What success looks like

You will be successful in this role if you can build an AI layer that is:

Accurate — answers are grounded in enterprise data.

Reliable — quality is measured through automated evaluation and regression testing.

Secure — agents and tools respect user permissions and data boundaries.

Scalable — models and retrieval infrastructure perform reliably in production.

Maintainable — AI capabilities are built as production software, not isolated experiments.

Useful — users can interact naturally with complex enterprise data.