Senior AI Engineer - LLM, RAG & Agent Systems

Senior AI Engineer – LLM, RAG & Agent Systems

About the Role

We are looking for aSenior AI Engineerto own the AI layer of our data platform—building production-gradeLLM 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 hasbuilt, deployed, and operated LLM systems in production.

What You’ll Own

• Design and build production-gradeLLM applications and RAG systems.
• Own retrieval architecture includingchunking, embeddings, vector search, hybrid search, and reranking.
• Buildagentic and tool-calling systemswith appropriate permissions, scoping, validation, and guardrails.
• Develop natural-language interfaces overenterprise data and structured databases.
• Build and maintainLLM 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 optimizeself-hosted open-weight modelsusing technologies such as vLLM or equivalent serving infrastructure.
• Optimize inference performance, GPU utilization, latency, throughput, and cost.
• Explore and implementfine-tuning or model adaptationwhen 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 least2 years of hands-on experience building and deploying production LLM-based systems.
• Strong Python engineering skills.
• Deep understanding ofRAG and retrieval architecture:
• Chunking strategies
• Embeddings
• Vector databases/search
• Hybrid search
• Reranking
• Retrieval evaluation
• Experience buildingLLM agents or tool-calling systems.
• Understanding ofpermissions, access control, scoping, validation, and guardrailsfor AI systems.
• Strong understanding ofLLM evaluation, including test datasets, regression testing, grounding, and hallucination detection.
• Experience working directly withdata 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 withself-hosted open-weight models.
• Production experience withvLLM or equivalent model-serving infrastructure.
• Understanding of GPU resource management and inference optimization.
• Experience withfine-tuning, LoRA, or other model-adaptation techniques.
• Experience withText-to-SQLsystems.
• Experience designing or using asemantic layerover 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.