**Target Profile:** * 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). **Project Overview \& Deliverables:** **Project Overview** * 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. **Deliverables:** * 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 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: 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 best practices in MLOps methodologies such as model versioning, reproducibility, metadata tracking, and experiment lineage.. **Important:** All candidates must pass an interview as part of the contracting process. We offer a pay range of $30\+ 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. We are looking for independent consultants \& contractors who run/operate their own business