Executive Director, Applied Artificial Intelligence Machine Learning

What you'll do

• Develop advanced agentic AI solutions using machine learning, deep learning, reinforcement learning, and optimization.
• Design robust agent architectures incorporating LLM reasoning, tool integration, state management, and loop engineering.
• Engineer reliable agent-driven workflows with a focus on correctness, traceability, and control-aware guardrails.
• Build knowledge-centric reasoning layers using knowledge graphs and hybrid retrieval methods to improve model grounding.
• Drive specification-driven development by authoring technical contracts and building evaluation and regression harnesses.
• Improve agent quality through automated critique loops, red-team feedback, and outcome-driven dataset curation.
• Coach and mentor AI/ML team members while maintaining high standards for engineering rigor and research depth.

What we're looking for

• PhD in a quantitative discipline, or an MS with at least 7 years of industry or research experience.
• At least 5 years of industry experience if the candidate does not hold a PhD.
• Extensive experience with machine learning and deep learning toolkits such as TensorFlow, PyTorch, NumPy, Scikit-Learn, and Pandas.
• Ability to design experiments, training frameworks, and evaluate model performance metrics aligned with business goals.
• Experience with big data and scalable model training in large scale distributed environments.
• Strong written and spoken communication skills to convey technical concepts to both technical and business audiences.
• Knowledge of advanced AI techniques including NLP, speech analytics, time series, reinforcement learning, and recommendation systems.
• Proficiency in developing production-quality code, including continuous integration models and unit test development.