ROLE RESPONSIBILITIES
What you’ll own.
- Build automated pipelines for model training, testing, evaluation, approval, deployment, rollback, and retraining.
- Implement model registries, experiment tracking, feature stores, model versioning, artifact management, and reproducibility controls.
- Configure model monitoring for drift, data-quality degradation, bias, performance, latency, reliability, and usage.
- Support A/B testing, champion-challenger deployment, model approval gates, release controls, and rollback procedures.
- Integrate MLOps pipelines with cloud platforms, source control, CI/CD, infrastructure-as-code, and security tooling.
- Develop model lifecycle documentation, operational runbooks, audit evidence, and governance workflows.
MINIMUM QUALIFICATIONS
Experience that prepares you for the work.
5+ years in DevOps, data engineering, ML engineering, software engineering, or cloud automation; 2+ years in MLOps or production ML delivery. Experience with MLflow, Azure Machine Learning, SageMaker, Vertex AI, Kubeflow, Databricks, or equivalent preferred.
CANDIDATE INFORMATION
Before you apply.
Engagement conditionsRemote, travel, client-site, clearance, compensation and benefit details are confirmed for the specific engagement during recruiting.
Accessible application processFor an application accommodation, contact info@jjtassoc.com or +1 470-256-0558.
Information handlingSubmit only recruiting information through this form. Do not include classified, controlled or export-restricted material.
