As a VP AI Engineer at the client, you will design and deploy agentic AI systems to automate and optimize large-scale production environments. The role focuses on building mission-critical LLM-powered solutions that can diagnose issues, reason over complex systems, and take secure, auditable actions. You will also develop and productionize AI capabilities using evaluation frameworks, prompt orchestration, response validation, and self-correction loops.
You will create RAG pipelines and domain knowledge systems with strong data quality, feedback loops, and governance. Responsibilities include integrating AI agents with observability, incident management, deployment, and runtime platforms, while ensuring safety, reliability, and compliance through policy enforcement and robust operational mechanisms such as rollback strategies and circuit breakers. You will optimize model and system performance, cost, and latency using techniques such as prompt engineering, caching, routing, batching, and streaming.
The ideal candidate has a quantitative or computational degree, with advanced study typically preferred, and at least 7+ years of experience in applied machine learning, data science, or software engineering within production environments. You should have strong hands-on development skills in Python (or related languages such as C++/Java/Go) and practical expertise with LLMs, including prompt engineering, fine-tuning or adaptation, RAG, tool-calling agents, and vector search. The client also expects experience building secure, explainable, and reliable AI systems with governance and auditability, along with the ability to collaborate across engineering, infrastructure, and operations teams to deliver measurable business outcomes.