Lead AI / ML Engineer

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 GCP).

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.