Machine Learning and Generative AI Engineer, Digital Transformation

Harvard Business School is part of Harvard University and is dedicated to advancing management education and thought leadership. They are seeking a Machine Learning and Generative AI Engineer to lead the development of innovative generative AI products, collaborating with various teams to operationalize AI models and optimize existing systems.

Responsibilities:

• Architect, build, maintain, and improve a suite of GenAI applications and their underlying systems
• Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA and other parameter-efficient methods
• Establish reusable frameworks to streamline model building, deployment and monitoring. Incorporate comprehensive logging, tracing, and alerting mechanisms
• Build guardrails, compliance rules, and oversight workflows into the GenAI application platform, including approval chains for model updates and staged rollouts for production releases
• Develop templates, guides, and sandbox environments to support onboarding of new contributors and experimentation with emerging techniques
• Ensure user-facing applications built on the GenAI application platform are safe and reliable, enforcing rigorous validation and testing before publishing, and implement a clear peer review process
• Apply an entrepreneurial mindset to identify opportunities to optimize business processes, improve user experiences, and prototype solutions that demonstrate value
• Work closely with data scientists and analysts to develop and deploy new product features across web and mobile applications
• Contribute to and promote sound software engineering practices across the team
• Mentor and educate team members to adopt best practices in writing and maintaining production-grade machine learning code
• Actively contribute to and leverage community best practices and open-source resources
• Monitor, debug, and resolve production issues in a timely manner
• Partner with project managers to ensure projects are delivered on time and within budget
• Collaborate with Technical Product Managers to track algorithmic performance KPIs and prioritize performance improvements based on effort and impact
• Build trust and collaboration by being present on-site and engaging directly with colleagues and various constituents
• Complete other responsibilities as assigned