About The RoleWe 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 LikeYou 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
