AI Agents Applied Research/Engineering Lead - Executive Director

Lead the end-to-end lifecycle of large language model (LLM)-based AI agent systems, including research, production deployment, and multi-step workflow orchestration.

Define and track success metrics such as task completion, accuracy, latency, and customer satisfaction for AI agents operating in a regulated financial domain.

Implement privacy, safety, security, and compliance controls (e.g., PCI compliance, auditability) while collaborating with cross-functional teams to bring AI systems to market.

Ph.D. with 8+ years or M.S. with 12+ years experience building and deploying AI systems in production.

Applied experience with LLMs including fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and system scaling techniques like caching and batching.

Strong foundation in machine learning, deep learning, experimental design, information retrieval, and recommendation systems.

Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn).

Technical leader with demonstrated ability to set and execute a technical research agenda from concept through production deployment in applied GenAI.

Experience developing conversational AI or LLM-based systems with multi-agent orchestration and practical expertise in reinforcement learning and agent safety evaluation.

Skilled at communicating complex research and technical strategies to senior leadership and non-technical stakeholders in a regulated, high-stakes environment.