Senior ML Ops Engineer — Remote & Scalable ML Pipelines

Sheetz; Pittsburgh (Allegheny County); On-site; USD 95,000 - 159,000; Full time

Benefits offered by this job

Quarterly bonuses

PTO and parental leave

401k match

Employee stock ownership

Tuition reimbursement

Medical, vision and dental coverage

Snack discounts

Job summary

Sheetz seeks a Senior Machine Learning Ops Engineer to design, deploy, and optimize ML infrastructure at scale. You will build robust pipelines that move models from development to production across stores and systems, ensuring reliability and performance.

The role emphasizes orchestration, CI/CD, cloud platforms, and observability, partnering with Data Science, Engineering, and DevOps teams to mature ML capabilities across the organization.

Qualifications

• Bachelor’s degree is required.
• Minimum 5 years hands-on experience in ML solutions with ML Ops.
• Experience with large databases and data pipelines.
• Experience deploying ML pipelines at scale and monitoring them.
• CI/CD for ML workflows and containerization is preferred.

Responsibilities

• Lead end-to-end development and optimization of ML pipelines at scale.
• Implement infrastructure for ML tools like MLflow, TensorFlow, PyTorch, Docker, Kubernetes.
• Design and monitor performance, drift, and alerting systems.
• Develop CI/CD pipelines for safe, rapid model iteration and retraining.
• Write high-quality production code for robust ML systems.
• Apply ML Ops best practices for reliable ML solutions.
• Collaborate with data science, engineering, and DevOps teams.
• Maintain documentation, versioning, and lineage for reproducibility.
• Improve ML infra, frameworks, and standards within the org.
• Mentor junior engineers on technical challenges and careers.

Skills

ML Ops

CI/CD

Data pipelines

Model deployment

Model monitoring

Cloud platforms

Version control

Documentation

Distributed systems

Automation

Education

Bachelor’s degree in Computer Science / MIS / Computer Engineering or related field

Tools

MLflow

TensorFlow

PyTorch

Docker

Kubernetes

Job description

Sheetz seeks a Senior Machine Learning Ops Engineer to design, deploy, and optimize ML infrastructure at scale. You will build robust pipelines that move models from development to production across stores and systems, ensuring reliability and performance.

The role emphasizes orchestration, CI/CD, cloud platforms, and observability, partnering with Data Science, Engineering, and DevOps teams to mature ML capabilities across the organization.