Machine Learning Engineer – MLOps Lead

Job Title:
Machine Learning Engineer – MLOps Lead

Duration: Contract role

Location: Remote, United States

You are being hired to product ionize machine learning at scale — eliminating fragile pilot models, building hardened MLOps pipelines, and delivering compliant, monitored, and continuously improving ML systems that directly support business operations.

Your success is measured not by “knowing tools,” but by deploying, stabilizing, and scaling real ML systems in production.

• Fully assess current ML pipelines, data flows, and deployment architecture
• Identify top 3 reliability, security, and performance risks in current ML lifecycle
• Produce a documented MLOps modernization roadmap

• Stand up standardized CI/CD pipelines for model training, validation, and deployment
• Implement automated monitoring, alerting, and versioning across active production models
• Deploy at least one business‑critical ML model into hardened production pipelines
• Establish security, audit, and compliance controls for model governance
• Reduce model deployment cycle time by 30–50%

• Operate a fully standardized enterprise MLOps framework (MLflow/Kubeflow/Airflow based)
• Enable continuous retraining and automated rollback capability
• Achieve ≥ 99.5% model uptime
• Establish retraining cadence that improves model accuracy and reliability quarter‑over‑quarter
• Mentor junior engineers and codify ML engineering standards

• Metric: Production model uptime —
  Target: ≥ 99.5%
• Metric: Model deployment cycle time —
  Target: ↓ 30–50%
• Metric: Automated pipeline coverage —
  Target: 100%
• Metric: Compliance audit readiness —
  Target: Continuous
• Metric: Model accuracy improvement —
  Target: QoQ measurable gains

• End‑to‑end MLOps pipelines (data → training → testing → deployment → monitoring → retraining)
• Kubernetes‑based model serving platforms
• Cloud ML platforms (Vertex AI / Sage Maker / Azure ML)
• CI/CD automation for ML systems
• Model observability and alerting using Prometheus / Grafana
• Secure, version‑controlled ML governance frameworks

• Proven delivery of production ML pipelines (not just experiments)
• Built CI/CD for ML models in Kubernetes environments
• Implemented monitoring, retraining, and version governance
• Delivered at least one enterprise‑scale ML deployment
• Hands‑on experience with MLflow / Kubeflow / Airflow
• Cloud ML production deployment (AWS, GCP, or Azure)
• Strong Python engineering background