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 LaTronic Solutions client work. Specific client requirements, schedules, security requirements, clearances, and other project details may vary by engagement.
LaTronic Solutions is committed to a professional and inclusive workplace. Employment decisions are based on qualifications, merit, business need, and the requirements of the applicable engagement.
