Machine Learning Ops Engineer II

Sheetz, Inc•Pittsburgh, PA

4d•Remote

About The Position

A Machine Learning Ops Engineer II at Sheetz ensures that AI models move seamlessly from “working on a laptop” to running reliably across our stores, applications, and systems at scale. This role powers capabilities like smarter inventory management, enhanced customer experiences, and faster decision-making that keeps pace with the way Sheetz operates. The MLOps Engineer designs, builds, and maintains the pipelines, deployment processes, and monitoring systems that allow models to run continuously and perform consistently. Just as Sheetz kitchens operate around the clock to serve customers, this role keeps our AI systems running 24/7, using data as the ingredients and algorithms as the recipes that drive our technology. This role qualifies for a remote work arrangement within our 7 state footprint (PA, OH, MI, WV, VA, MD, NC).

Requirements

Bachelor’s degree in Computer Science, Management Information Systems, Computer Engineering, or related discipline is required
Minimum 3 years experience in design, development, and deployment of ML solutions required
Previous utilization of programming languages (Python, Bash) or scripting for automation and ML pipeline orchestration preferred
Previous experience in machine learning pipelines, model lifecycle management, or MLOps concepts (e.g., model deployment, monitoring, CI/CD) preferred
Previous experience in secure development practices and cloud environments (e.g., AWS, GCP, or Azure) preferred

Nice To Haves

Certifications in cloud platforms (AWS/GCP/Azure), ML Ops, or DevOps tools preferred.

Responsibilities

Contribute to the design, automation, and maintenance of end-to-end machine learning pipelines, including model training, validation, deployment, and monitoring
Write well-structured, testable, and maintainable code to support robust ML systems
Apply software engineering best practices to productionize machine learning workflows
Collaborate with internal teams to build, integrate, and scale machine learning solutions that align with business and operational requirements
Utilize tools including but not limited to MLflow, TensorFlow, PyTorch, and containerization frameworks (e.g., Docker, Kubernetes) to deploy and manage models in production environments
Monitor deployed models for drift, latency, and performance degradation; implement alerting and retraining pipelines as needed to maintain reliability, escalating as required
Assist in the setup and optimization of CI/CD pipelines for ML workflows to enable fast and safe model iteration and deployment
Maintain documentation, version control, and metadata tracking to ensure models are reproducible and auditable
Recommend improvements to MLOps practices, frameworks, and tooling and help to define, and refine, operational standards, as the organization’s ML capabilities mature