Junior Machine Learning Engineer Jobs in Racine, WI

Job Summary:
Harley-Davidson Motor Company is a storied brand founded in 1903, known for its passion and commitment to innovation. They are seeking a Senior ML/GenAI Ops Engineer to design, develop, and operationalize machine learning and generative AI platforms, ensuring seamless integration into production environments with a focus on scalability and compliance.
Responsibilities:
• 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.
• 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.
• 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 DevOps.
• 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.
• 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.
Qualifications:
Required:
• High School Diploma or Equivalent Required
• 7+ years of experience in data engineering or DevOps 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
• Proficiency in Azure Cloud Platform, specifically Azure ML Studio and Azure AI Foundry
• Proficiency in Python, SQL, and ML/AI DevOps tools (e.g., MLflow, scikit learn, PyTorch, Kubeflow, TensorFlow Extended).
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and containerization/orchestration tools (Docker, Kubernetes).
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and data pipeline tools (e.g., Apache Airflow, dbt).
• Proficiency with vector databases, LLM workflows, or RAG pipelines.
• Familiarity with cost management, autoscaling, and GPU governance in Azure ML.
• Experience with data governance frameworks and security best practices.
• Technical Acumen: Strong knowledge of ML/AI lifecycle management, MLOps practices, and data pipeline optimization.
• Collaboration & Communication: Excellent teamwork skills with an ability to work closely with cross-functional teams and communicate complex technical concepts effectively.
• Problem-Solving: Proactive approach & proven ability to identifying and solve issues in model performance, data quality, and infrastructure bottlenecks.
• Ethics and Compliance: Deep understanding of responsible AI practices, including bias detection, explainability, and data privacy.
• Governance & Data Integrity: Ability to enforce data privacy, lineage, and data quality controls across ML workflows, ensuring compliance with enterprise and regulatory requirements.
Preferred:
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field is preferred
• Azure AZ-900 certification, with additional ML/LLM/RAG focused certifications preferred.
Company:
In 1903, out of a small shed in Milwaukee, Wisconsin, four young men lit a cultural wildfire that would grow and spread across geographies and generations. Founded in 1903, the company is headquartered in Milwaukee, USA, with a team of 5001-10000 employees. The company is currently Late Stage.