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.