Senior AI Engineer – LLM, RAG & Agent Systems

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.