Machine Learning Ops Engineer II

Sheetz
Pittsburgh, United States of America
6 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate
Compensation
$ 131K

Job location

Remote
Pittsburgh, United States of America

Tech stack

Clean Code Principles
Adobe InDesign
Artificial Intelligence
Amazon Web Services (AWS)
Azure
Bash
Computer Engineering
Continuous Integration
DevOps
Monitoring of Systems
Python
Machine Learning
TensorFlow
Secure Coding
Scripting (Bash/Python/Go/Ruby)
Cloud Platform System
PyTorch
Containerization
Kubernetes
Information Technology
Machine Learning Operations
Software Version Control
Data Pipelines
Docker
Programming Languages

Job description

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:

  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

Requirements

(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

Benefits & conditions

Tuition reimbursement, Parental leave, Health insurance, 401(k) matching, Paid time off, Vision insurance, Dental insurance, 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!

Apply for this position