Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.

Job Description:

This position conducts the support, maintenance, and monitoring of Machine Learning (ML) models and software components that solve challenging business problems for the organization, working in collaboration with the Business, Product, Architecture, Engineering, and Data Science teams. This position supervises and engages in assessment and analysis of large-scale data sources of structured and unstructured data (internal and external) to uncover opportunities for ML and Artificial Intelligence (AI) automation. This position works with teams, or individually, to debug, develop minor enhancements, and complete other tasks related to ML/AI environments.

RESPONSIBILITIES

• Deploy and manage machine learning models in production environments, ensuring smooth integration with existing systems
• Monitor model performance using established metrics (accuracy, precision, recall, F1 score, etc.) and identify potential issues like performance degradation, drift, or bias
• Respond to and resolve production incidents promptly, minimizing downtime and impact on business operations
• Collaborate with stakeholders (data scientists, software engineers, operations) to diagnose and fix problems
• Develop and implement contingency plans for model failures or unexpected events
• Maintain documentation for deployed models, including deployment logs, monitoring dashboards, and troubleshooting procedures
• Ensure model compatibility with new software versions and infrastructure changes.
• Optimize model resource utilization (memory, CPU) to reduce costs and improve efficiency.
• Archive and manage different model versions for auditability and rollback capabilities.
• Analyze logs, error messages, and other data to diagnose model issues.
• Utilize debugging tools and techniques specific to machine learning (profiling, feature importance analysis, anomaly detection).
• Reproduce and isolate issues in test environments before fixing them in production.
• Document bug fixes and lessons learned to improve future model development and maintenance.
• Analyze monitoring data proactively to identify trends and potential issues before they impact production.
• Stay up-to-date on new monitoring tools and techniques for machine learning systems.
• Report on model performance and health to stakeholders regularly.
• Stay up-to-date on advances in machine learning and best practices for production usage.
• Contribute to documentation and knowledge sharing within the team.

QUALIFICATIONS

Primary:

• Demonstrated proficiency in Python, SQL, and GCP.
• In-depth experience in DevOps practices for supporting and maintaining applications and products driven by Machine Learning (ML) and Artificial Intelligence (AI).
• Expertise in developing and optimizing ML pipelines.
• Understanding and experience in scaling solutions for ML and AI applications.
• Demonstrate advanced expertise in various ML frameworks such as Python, Scala or Java for debugging and developing minor enhancements to models.
• Proficiency in deploying and managing models using Kubernetes
• Experience in supporting products with capabilities including Deep Learning, Supervised, and Unsupervised techniques.
• Ability to implement solutions using Python, Scala, or Java.
• Ability to utilize visualization tools for model monitoring and management in production.
• Experience in deploying and managing machine learning models using containerization tools such as Kubernetes, Docker, Fargate, etc.
• Strong documentation and communication skills for technical and non-technical stakeholders.
• Strong investigative and troubleshooting skills with regards to ML Pipelines.

Secondary:

• Knowledgeable in data visualization tools such as Power BI (PBI), Looker, and Tableau.
• Experience in advanced model monitoring and management techniques.
• Comfortable working with data scientists, data engineers, product owners and architects
• Strong background in Agile/Kanban development methodologies.
• Proven ability to work within Agile Frameworks for ML and AI projects.

Employee Type:

UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.