eNGINE
DevOps Engineer | Machine Learning Platforms (Pittsburgh)
eNGINE, Pittsburgh, Pennsylvania, United States, 15289
ML Ops Engineer
Remote | Pittsburgh, PA area
On-site: 1 day/month
About eNGINE eNGINE 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 eNGINE.
Role Overview eNGINE is seeking a MLOps Engineer to manage and scale machine learning workflows from development to production. This role ensures that models are robust, maintainable, and performant in real-world environments, while collaborating closely with Data Science and Engineering teams to integrate ML solutions into digital products.
Key Responsibilities Implement end-to-end ML deployment strategies to move models from development to production reliably Configure and manage scalable, cloud-based infrastructure for ML workloads Track and analyze model behavior and operational metrics to ensure consistent performance Establish automated processes for retraining, versioning, and releasing ML models Work closely with cross-functional teams to embed machine learning capabilities into applications and platforms Review and refine system architecture and pipelines to improve latency, throughput, and resource utilization Maintain documentation and operational standards for reproducible, production-ready ML systems Identify and apply new tools and technologies to streamline ML operations and reduce maintenance overhead
Required Qualifications Bachelors degree Experience deploying machine learning solutions in production environments Strong Python skills, including experience with numerical and ML libraries (NumPy, Pandas, scikit-learn) and at least one deep learning framework (PyTorch or TensorFlow) Experience with containerization and orchestration technologies such as Docker and Kubernetes Knowledge of cloud platforms (AWS, GCP, or Azure) and Infrastructure-as-Code tools Familiarity with ML workflow management or experiment tracking tools (MLflow, Kubeflow, or similar) Understanding of software engineering best practices, including version control, testing, and documentation
Preferred Experience Prior involvement in building or supporting ML-driven digital products Experience optimizing ML pipelines for cost, performance, and scalability Collaborative experience with cross-functional engineering and data teams Practical exposure to monitoring, alerting, and incident response for ML systems
Location & Work Environment Fully remote, with monthly on-site meetings in the Pittsburgh, PA area
Next Steps For finer details on how eNGINE can enhance your career, apply today! No C2C, third-party candidates, relocation assistance, or sponsorship available for this role.
About eNGINE eNGINE 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 eNGINE.
Role Overview eNGINE is seeking a MLOps Engineer to manage and scale machine learning workflows from development to production. This role ensures that models are robust, maintainable, and performant in real-world environments, while collaborating closely with Data Science and Engineering teams to integrate ML solutions into digital products.
Key Responsibilities Implement end-to-end ML deployment strategies to move models from development to production reliably Configure and manage scalable, cloud-based infrastructure for ML workloads Track and analyze model behavior and operational metrics to ensure consistent performance Establish automated processes for retraining, versioning, and releasing ML models Work closely with cross-functional teams to embed machine learning capabilities into applications and platforms Review and refine system architecture and pipelines to improve latency, throughput, and resource utilization Maintain documentation and operational standards for reproducible, production-ready ML systems Identify and apply new tools and technologies to streamline ML operations and reduce maintenance overhead
Required Qualifications Bachelors degree Experience deploying machine learning solutions in production environments Strong Python skills, including experience with numerical and ML libraries (NumPy, Pandas, scikit-learn) and at least one deep learning framework (PyTorch or TensorFlow) Experience with containerization and orchestration technologies such as Docker and Kubernetes Knowledge of cloud platforms (AWS, GCP, or Azure) and Infrastructure-as-Code tools Familiarity with ML workflow management or experiment tracking tools (MLflow, Kubeflow, or similar) Understanding of software engineering best practices, including version control, testing, and documentation
Preferred Experience Prior involvement in building or supporting ML-driven digital products Experience optimizing ML pipelines for cost, performance, and scalability Collaborative experience with cross-functional engineering and data teams Practical exposure to monitoring, alerting, and incident response for ML systems
Location & Work Environment Fully remote, with monthly on-site meetings in the Pittsburgh, PA area
Next Steps For finer details on how eNGINE can enhance your career, apply today! No C2C, third-party candidates, relocation assistance, or sponsorship available for this role.