Senior ML​/GenAI Ops Engineer - Milwaukee, WI

Overview

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Title:

Senior ML/GenAI Ops Engineer - Milwaukee, WI
Job Function:
Digital

Location:

JUNEAU
Workplace Category:
Onsite
Company:
Harley-Davidson Motor Company
Full or Part-Time:
Full Time
Shift: SHIFT1

At Harley-Davidson, we are building more than machines. It’s our passion and commitment to continue the evolution of this storied brand, and heighten the desirability of the Harley-Davidson experience. To keep building our legend and leading our industry through innovation, evolution, and emotion we need the best and brightest talent. We stand for the timeless pursuit of adventure. Freedom for the soul.

Are you ready to join us?

Harley-Davidson Motor Company, founded in a humble Milwaukee backyard shed in 1903, still calls the city home. Today, its Corporate Campus includes a 4.8-acre public park—a welcoming green space open to all. Join our team as aSr Data Engineer.

Job Summary:

We are looking for a skilled Sr. Data Engineer - ML & AI Operations to join our growing team. In this role, you will be responsible for designing, developing, and deploying & operationalizing machine learning and generative AI (GenAI) platforms to deliver high-impact solutions to business challenges and optimize processes. This role focuses on the operationalization and automation of machine learning and AI solutions, ensuring they are seamlessly integrated into production environments with a high degree of scalability, reliability, and compliance with ethical guidelines.

The ideal candidate will bring strong technical expertise in data engineering, a deep understanding of ML and AI Dev Ops best practices, and a commitment to building robust, maintainable systems. You will lead the design, development, and scaling of data pipelines, ML infrastructure, and AI production systems that power models used across the business. If you are passionate about creating and operationalizing transformative ML and AI solutions, we’d love to hear from you!

Key Responsibilities:

• Platform Design & Development:
• Design, develop, and maintain scalable platforms for machine learning and GenAI, supporting end-to-end processes from data ingestion to model deployment and monitoring.
• Lead end-to-end solution design for ML/AI data pipelines and model-serving platforms, ensuring architectures meet scalability, reliability, and regulatory requirements.
• Partner closely with project and program managers to establish delivery timelines, resource plans, and milestone tracking for complex, multi-team data/ML efforts.
• Champion best practices for reproducibility, automation, observability, and governance/COE in ML/AI operational pipelines and platforms.
• Oversee compute governance, alert monitoring and model lifecycle.

• Model Deployment & Automation:
• Implement CI/CD pipelines for automated deployment of ML and AI models to production environments.
• Work closely with data scientists to ensure model readiness and optimization, focusing on robust deployment and monitoring.
• Develop and manage tools for continuous monitoring and performance management of models post-deployment to identify and resolve performance drift.

• Collaboration and Business Alignment:
• Partner with data scientists, software engineers, product owners, and stakeholders to align ML and AI solutions with business goals and performance metrics.
• Facilitate seamless integration of ML/AI systems with business processes, ensuring data accessibility, quality, and real-time insights.

• Operationalization & Maintenance:
• Ensure systems are built for scalability, maintainability, and security, adhering to best practices in ML & AI Dev Ops.
• Implement monitoring solutions to proactively address any issues in data, model performance, or infrastructure.
• Drive architectural reviews, design decisions, and engineering standards that support long-term operational excellence for ML/AI workloads.
• Serve as the primary technical escalation point for delivery risks and system performance issues, ensuring timely resolution and stakeholder alignment.

• Ethics and Compliance:
• Integrate AI ethics and compliance considerations into all ML/AI solutions, with a focus on data privacy, bias detection, and model transparency.
• Implement processes to…