Job description

Auto req : 30671 Title : Senior Data Engineer - ML & AI Job Function : Information Technology Location : Remote Company: Harley-Davidson Motor Company Full or Part-Time: Full Time 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? This remote role is not tightly linked to a physical location and provides flexibility in where, when and how you accomplish your work. Remote employees are expected to have a dedicated, quiet and distraction-free work space and an internet connection that’s sufficient for completing their job remotely. 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 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. 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. • 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. • 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. 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 meet regulatory requirements and promote responsible AI use. Education Requirements • High school diploma or equivalent is required. • Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field is preferred. Qualifications and Requirements Experience: • 5+ years of experience in data engineering or Dev Ops roles, with a focus on ML/AI platforms and infrastructure. • Proven experience in operationalizing and automating ML and GenAI solutions in production environments. • Strong experience with cloud platforms (AWS, Azure, GCP) and managing infrastructure for data and machine learning systems. Technical Skills: • Proficiency in Python, SQL, and ML/AI Dev Ops tools (e.g., MLflow, Kubeflow, Tensor Flow Extended). • Experience with CI/CD tools (e.g., Jenkins, Git Lab CI) and containerization/orchestration tools (Docker, Kubernetes). • Familiarity with machine learning frameworks (e.g., Tensor Flow, PyTorch) and data pipeline tools (e.g., Apache Airflow, dbt). Key Skills and Competencies • Technical Acumen: Strong…

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