Lead AI/ML Engineer

Role: Lead AI/ML Engineer

Location: 100% Remote, USA

Primary Responsibilities:

AI Project Execution & Delivery:

• Lead the end-to-end execution of high-priority AI/ML projects, ensuring they are delivered on time, within budget, and to the highest technical standards.
• Translate the enterprise AI strategy and product roadmaps into detailed project plans, technical specifications, and actionable backlogs for engineering teams.
• Serve as the primary technical point of contact for project stakeholders, managing dependencies, mitigating risks, and communicating progress effectively.

AI Governance & AIRB Facilitation:

• Manage the day-to-day operations of the AI Review Board (AIRB) submission process, acting as a hands-on guide for Data Science and product teams.
• Facilitate the preparation of all required documentation for AIRB reviews, ensuring submissions are complete, clear, and proactively address potential ethical, compliance, and technical concerns.
• Implement and enforce the governance framework, ensuring teams adhere to established standards and best practices for responsible AI.

Team Leadership & Technical Mentorship:

• Provide direct line management, technical leadership, and mentorship to a team of senior AI/ML Engineers and Data Scientists.
• Foster a culture of engineering excellence, collaboration, and continuous improvement within the team and enterprise.
• Conduct code reviews, design sessions, and technical deep dives to ensure the quality, scalability, and robustness of AI solutions.

Hands-on MLOps & Engineering Practice:

• Drive the practical implementation of the MLOps strategy, directly overseeing the construction and optimization of CI/CD pipelines for AI/ML systems using tools like GitHub Actions.
• Enforce rigorous engineering hygiene, including version control for code, data, and models (Git, DVC), and the application of Infrastructure as Code (IaC) principles.
• Lead the technical implementation of production monitoring solutions to track model performance, identify drift, and ensure the long-term reliability of deployed AI systems.

Required Qualifications:

• Proven AI/ML Leadership: 10-15 years of experience in the AI/ML field, with at least 4-5 years in a leadership or management role leading technical teams in the delivery of complex AI solutions.
• Experience with AI Governance: Direct, hands-on experience successfully navigating an internal AI ethics, risk, or governance review process for multiple projects.
• Strong Project Management Skills: Demonstrated ability to manage complex technical projects from conception to deployment, with expertise in agile methodologies.
• Expertise in the ML Lifecycle: Deep, practical knowledge of the entire machine learning lifecycle, from data acquisition and feature engineering to model deployment and post-launch monitoring.
• Hands-on MLOps Experience: Proven experience building and managing CI/CD pipelines and MLOps workflows for machine learning.
• Strong Technical Foundation: Proficient in Python, common ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), and cloud platforms (AWS, Azure, or Google Cloud Platform).

Preferred Qualifications:

• Advanced Degree: A Master's or Ph.D. in a relevant quantitative field.
• Healthcare Domain Experience: Experience developing and deploying AI/ML solutions within a healthcare or other highly regulated environment.
• Product Mindset: Experience working closely with product managers to define and deliver AI-powered features and products.
• Expertise in GenAI Operations: Specific experience in the operational challenges of deploying and managing LLM-based applications in production.
• Mentorship and Talent Development: A passion for coaching and developing technical talent, with a track record of growing senior engineers into tech leads.