The client is seeking a VP AI Engineer to design, develop, and deploy agentic AI systems that automate and optimize large-scale production environments. This role will build LLM-powered solutions that can diagnose issues, reason over complex systems, and take secure, auditable actions to support mission-critical, high-reliability operations. The position focuses on modernizing large-scale production systems by combining software engineering, data science, and applied AI.
Key responsibilities include developing RAG pipelines and domain knowledge systems with strong data quality, feedback loops, and governance; productionizing LLM capabilities through evaluation frameworks, prompt orchestration, response validation, and self-correction loops; and integrating AI agents with observability, incident management, deployment, and runtime platforms. The role is expected to implement safety, reliability, and compliance guardrails such as policy enforcement, rollback strategies, and circuit breakers, while optimizing performance, cost, and latency through prompt engineering, caching, routing, batching, and streaming.
The ideal candidate will have a bachelor’s degree in a quantitative or computational field, with a master’s or PhD preferred. Experience of 7+ years in applied ML, data science, or software engineering within production environments is expected, along with strong hands-on development in Python (or C++/Java/Go) for building large-scale applications. Practical expertise with LLMs—including prompt engineering, fine-tuning or adaptation, RAG, tool-calling agents, and vector search—is required, as is a track record of building secure, explainable, and reliable AI systems with governance and auditability, plus the ability to collaborate across engineering, infrastructure, and operations to deliver measurable business outcomes.