This position offers a base salary range of $78,807.00 - $131,346.00 per year, depending on experience and qualifications, plus bonus based on company performance. One of the MANY work perkz at Sheetz is quarterly employee bonuses based on company performance! And there’s more – A LOT more… like competitive salaries, PTO and parental leave, 401k match and employee stock ownership, limitless professional development and growth opportunities, tuition reimbursement, full medical, vision and dental coverage, and snack discounts! 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). OVERVIEW Support the design, development, and deployment of ML solutions and infrastructure to operationalize machine learning models and ensure their performance at scale. Maintain robust, reproducible, and scalable machine learning workflows, monitor model health in production, and assist in implementing MLOps best practices. Utilize experience and gain technical depth to contribute to the ongoing maturity of the ML ecosystem across the organization. RESPONSIBILITIES (other duties may be assigned) 1. Contribute to the design, automation, and maintenance of end-to-end machine learning pipelines, including model training, validation, deployment, and monitoring 2. Write well-structured, testable, and maintainable code to support robust ML systems 3. Apply software engineering best practices to productionize machine learning workflows 4. Collaborate with internal teams to build, integrate, and scale machine learning solutions that align with business and operational requirements 5. 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 6. Monitor deployed models for drift, latency, and performance degradation; implement alerting and retraining pipelines as needed to maintain reliability, escalating as required 7. Assist in the setup and optimization of CI/CD pipelines for ML workflows to enable fast and safe model iteration and deployment 8. Maintain documentation, version control, and metadata tracking to ensure models are reproducible and auditable 9. Recommend improvements to MLOps practices, frameworks, and tooling and help to define, and refine, operational standards, as the organization’s ML capabilities mature QUALIFICATIONS (Equivalent combinations of education, licenses, certifications and/or experience may be considered) Education • Bachelor’s degree in Computer Science, Management Information Systems, Computer Engineering, or related discipline is required Experience • 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 Licenses/Certifications • Certifications in cloud platforms (AWS/GCP/Azure), ML Ops, or DevOps tools preferred. Tools & Equipment • General Office Equipment ACCOMMODATIONS Sheetz is committed to the full inclusion of all qualified individuals. Sheetz is committed to considering all applicants regardless of disability who can perform all essential job duties with or without accommodations.

• Build, ship, and own product features end-to-end • Work with designers, and product managers to create high-performing product features. • Apply a range of techniques—from classical ML to LLM-based approaches (RAG, prompt engineering, fine-tuning, semantic search)—with a strong focus on reliability, performance, and maintainability • Write well-designed, maintainable, and testable code • Write clear and well-defined design documentation • Troubleshoot, debug, and resolve software bugs • Be product-minded and think about the customer • Stay updated on AI/ML advancements and explore new techniques and tools. • Participate in an Agile software development life cycle • Work with Python, JavaScript, Node.JS, Docker, PostgreSQL, Kubernetes, etc

• Participating in the design and development of AI solutions for challenging projects. • Building production level ML/AI solutions, with solid software engineering and ML/AI principles. • MLOps Automated deployment and monitoring (models and infrastructure). • Data analysis (data cleaning, variable transformation, etc.). • Developing and training ML models. • Putting AI models into production. • This means parallelizing, optimizing, tuning, testing the models to deploy in a production environment.

• Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems. • Build and manage efficient pipelines for rapid experimentation and hypothesis testing. • Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations. • Deploy models into production environments using CI/CD practices and model serving frameworks. • Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability. • Apply modern techniques to optimize models for inference speed, memory footprint, and cost. • Lead efforts in dataset creation, augmentation, and curation to build high-quality, robust training data. • Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs).