Note: The job is a remote job and is open to candidates in USA. Sheetz is a company that values innovative solutions and employee growth. They are seeking a Senior Machine Learning Ops Engineer to ensure AI models operate reliably across their systems, enhancing inventory management and customer experiences while maintaining ML pipelines and infrastructure.
Responsibilities
- Lead the end-to-end development and optimization of ML pipelines, including training, validation, deployment, monitoring, and retraining workflows at scale
- Guide the use of and implement infrastructure for tools such as ML flow, TensorFlow, PyTorch, Docker, and Kubernetes to support scalable production workflows for model deployment and lifecycle management
- Design and monitor tools for performance monitoring, drift detection, and automated alerting
- Develop CI/CD pipelines to enable safe, rapid model iteration, deployment, and retraining across environments
- Write, review, and maintain high-quality, production ready code, ensuring robust, reproducible, and secure ML systems
- Apply advanced software engineering and ML Ops best practices to operationalize machine learning solutions efficiently and reliably
- Collaborate with cross-functional teams to align ML solutions with business needs and system requirements and guide integration efforts to embed ML into production applications
- Maintain thorough documentation, version control, metadata tracking, and lineage to support reproducibility and compliance of ML models
- Recommend and implement improvements to ML infrastructure, frameworks, and operational standards, elevating the organization’s ML maturity and capabilities
- Mentor and coach junior engineers, providing guidance on technical challenges, workflow design, and career development
Skills
- Bachelor's degree in Computer Science, Management Information Systems, Computer Engineering, or related discipline
- Minimum 5 years hands-on experience in designing, developing, and operationalizing machine learning solutions, with a strong focus on ML Ops practices and infrastructure
- Previous experience working with large databases – both structured and unstructured – to build data pipelines and self-service dashboards for business users
- Previous experience in managing machine learning pipelines, lifecycle management, and deployment at scale—including training, validation, serving, and monitoring
- Previous experience with CI/CD pipelines for ML workflows and containerization tools such as Docker and Kubernetes
- Previous experience with secure and scalable cloud environments (e.g., AWS, GCP, Azure) and infrastructure-as-code and platform-as-a-service (PaaS) offerings
- Cloud Platforms (AWS, GCP, Azure)
- MLOps tools and frameworks (e.g., ML Flow, Kubeflow, TFX)
- DevOps certifications (e.g. Docker, Kubernetes, Terraform, CI/CD Tools)
Benefits
- Quarterly employee bonuses based on company performance
- 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
- Snack discounts
- Remote work arrangement within our 7 state footprint (PA, OH, MI, WV, VA, MD, NC)
Company Overview
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