ML Ops Engineer Specialist

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