Senior AI Engineer - LLM, RAG & Agent Systems

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.