DevOps Engineer | Machine Learning Platforms

MLOps Engineer (Remote | Pittsburgh, PA area)On-site: 1 day/monthWe are seeking a highly skilled MLOps Engineer to support the end-to-end deployment, monitoring, and optimization of our machine learning models. In this role, you will serve as the critical link between Data Science and Operations, ensuring that models are scalable, reliable, and production-ready. This position is fully remote, but candidates must reside in the Pittsburgh area and be available for monthly on-site meetings. About eNGINEeNGINE builds Technical Teams.We are a Solutions and Placement firm shaped by decades of interaction with Technical professionals. Our inspiration is continuous learning and engagement with the markets we serve, the talent we represent, and the teams we build. Our Consulting Workforce is encouraged to enjoy career fulfillment in the form of challenging projects, schedule flexibility, and paid training/certifications. Successful outcomes start and finish with eNGINEKey Responsibilities• Pipeline Development: Design, build, and maintain CI/CD pipelines supporting the full machine learning lifecycle, from training to deployment.• Infrastructure Management: Orchestrate and maintain containerized environments using Docker and Kubernetes; manage cloud resources for scalable and efficient inference. • Model Monitoring: Build systems to monitor model performance, detect data drift, ensure uptime, and maintain compliance with reliability standards. • Automation: Automate training, testing, deployment, and retraining processes to reduce manual steps and increase operational efficiency. • Collaboration: Partner with Data Scientists, Software Engineers, and Product teams to integrate ML into production systems and support ongoing enhancements.• Optimization: Continuously evaluate model pipelines and infrastructure for improvements in cost, performance, and scalability. Technical Requirements• Programming: Expert-level Python, including NumPy, Pandas, scikit-learn, and at least one major deep learning framework (PyTorch or TensorFlow). • Infrastructure: Strong hands-on experience with Docker, Kubernetes, and IaC tools such as Terraform or CloudFormation. • MLOps Tooling: Familiarity with MLflow, Kubeflow, or similar model management platforms.• Cloud Platforms: Practical experience working with AWS, GCP, or Azure ML services. • Best Practices: Solid understanding of version control, automated testing, documentation, and reproducible ML workflows. Qualifications• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related technical field. • Proven experience deploying machine learning models to production environments—not just experimentation. • Prior experience supporting or building ML-driven digital products strongly preferred.• Digital product / platform experience• Demonstrated ability to work effectively across cross-functional engineering and data teams. • Strong problem-solving abilities, attention to detail, and a passion for building stable, scalable ML systems. Next StepsNo C2C, relocation, or sponsorship for this roleFor finer details on how eNGINE can impact your career, ! Apply tot his job