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.