Position Overview
Implements the automated processes, infrastructure, governance, and monitoring needed to reliably deploy, manage, retrain, validate, and retire machine-learning models.
Key Responsibilities
- 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
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.
About This Opportunity
This is a full-time remote position supporting current and upcoming JJT & Associates client work. Specific client requirements, schedules, security requirements, clearances, and other project details may vary by engagement.
JJT & Associates is committed to a professional and inclusive workplace. Employment decisions are based on qualifications, merit, business need, and the requirements of the applicable engagement.