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ML Ops Engineer Specialist at Invisible Technologies. Location Information: World Wide - Remote. . What You’ll Do. . . . You’ll design and implement robust infrastructure to enable scalable, reliable, and reproducible machine learning workflows. You’ll streamline the lifecycle of ML models, from experimentation to deployment, ensuring our systems are production-grade and future-proof.. . . Build Scalable ML Infrastructure:. Architect, deploy, and maintain . pipelines. and tooling that support versioning, training, testing, and deployment of machine learning models across a variety of environments.. . Bridge Research and Production:. Work closely with ML researchers, data scientists, and backend engineers to translate prototypes into efficient, production-ready services and APIs.. . Focus on Automation and Reliability:. Implement systems for continuous integration, model monitoring, auto-scaling, and failover, with a strong emphasis on observability and operational excellence.. . Optimize Cloud Resources:. Manage and optimize compute resources across cloud and hybrid environments (e.g., GCP, AWS, on-prem), reducing latency and cost while maintaining high reliability.. . Document Best Practices:. Document and deliver best practices in . MLOps. methodologies such as model versioning, reproducibility, metadata tracking, and experiment lineage... . . What We Need. . . . Professional Experience:. . . 2+ years of experience building and maintaining ML infrastructure or platforms in production environments.. . Demonstrated ability to take ML models from experimentation to deployment using MLOps best practices.. . Experience collaborating with data scientists, ML engineers, and backend teams on cross-functional projects.. . . Technical Expertise:. . . Proficiency in Python and core ML tooling (e.g., MLflow, Kubeflow, Airflow, Docker, Git).. . Familiarity with model training frameworks such as . PyTorch. , ONNX, or scikit-learn.. . Experience with CI/CD pipelines tailored to ML systems (e.g., model validation checks, artifact versioning).. . Comfortable managing infrastructure via cloud services (GCP, AWS) and container orchestration platforms (e.g., Kubernetes).. . Strong debugging and performance tuning skills across data, model, and infrastructure layers.. . . Bonus (Nice to Haves):. . . Hands-on experience with Databricks or similar distributed compute environments.. . Familiarity with data engineering tools and workflow orchestration (Spark, dbt, Prefect).. . Knowledge of monitoring and observability stacks (Prometheus, Grafana, OpenTelemetry) for ML systems.. . Exposure to regulatory/compliance-aware ML deployment (audit logs, reproducibility, rollback strategies).. . . . . . We offer a pay range of $35-to- $50 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor you’ll supply a secure computer and high‐speed internet; company‐sponsored benefits such as health insurance and PTO do not apply.. . Important:. . All candidates must pass an interview as part of the contracting process.. . .