Director of AI/ML

About the role

We are seeking a Director of AI/ML to lead and scale advanced artificial intelligence capabilities within a mission‑driven healthcare technology organization. The role combines strategic leadership with hands‑on technical oversight to deliver AI solutions that improve patient outcomes.

Key responsibilities

• Lead and manage a multidisciplinary AI/ML organization, including AI engineers, data engineers, and AI architects.
• Define team structure, hire top talent, and drive performance, career development, and technical mentorship.
• Partner with business and functional stakeholders to identify high‑value AI use cases and translate needs into scalable solutions.
• Oversee the full AI/ML lifecycle from problem framing to deployment, monitoring, and continuous optimization.
• Ensure AI systems are scalable, reliable, explainable, and aligned with responsible AI and MLOps best practices.
• Define KPIs and success metrics for AI initiatives, tracking business impact, ROI, and operational performance.
• Coordinate cross‑functionally with product, engineering, IT, security, legal, and compliance teams to deliver compliant AI solutions.
• Communicate complex AI concepts clearly to senior and executive stakeholders, translating technical outcomes into business value.
• Lead initiatives involving advanced AI approaches such as LLMs, transformer models, RAG systems, and agentic AI architectures.
• Drive continuous improvement and innovation across AI programs in regulated, enterprise‑scale environments.

Required profile

• Bachelor’s degree or higher in Computer Science, AI, Machine Learning, Data Science, or related field (or equivalent experience).
• 10+ years of professional experience in AI/ML or data science, including delivery of production‑grade AI systems.
• 3+ years of people‑management experience leading technical teams.
• Strong expertise in classical machine learning techniques such as deep learning, decision trees, random forests, statistical modeling, and optimization.
• Hands‑on experience with modern AI systems, including large language models, transformer architectures, retrieval‑augmented generation, and agentic AI.

Required skills

• Deep learning
• Decision trees
• Random forests
• Statistical modeling
• Optimization
• Large language models (LLMs)
• Transformer models
• Retrieval‑augmented generation (RAG) systems
• Agentic AI architectures
• MLOps best practices