Position: Lead AI/ML Engineer

Remote

Note:  Need Exceptional exp in AI/ML concepts (GenAI, Agentic, RAG), Vector DB works, news recommendation system using GenAI) or provide details about existing projects like claims processing. metadata extraction

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.