Senior AI/ML & MLOps Engineer - Remote

DecisionPoint | Cortek; Oregon; Hybride; USD 140 000 - 190 000; Plein temps

Résumé du poste

DecisionPoint Corporation is seeking a Senior AI/ML & MLOps Engineer to build, deploy, monitor, maintain, and govern production ML capabilities for the USMEPCOM ENLIST program. The role focuses on training data, automated pipelines, model promotion, and secure governance across development, testing, and production environments.

The candidate will work with data scientists, cloud architects, cybersecurity personnel, testers, and government mission owners to ensure repeatable, traceable ML

Qualifications

• Active Secret clearance or higher.
• Bachelor's degree in computer science, data science, engineering, or a related field, or a permitted equivalent.
• Minimum of 10 years of relevant professional experience.
• Experience maintaining machine learning training, validation, and testing datasets.
• Experience building or maintaining automated machine learning deployment pipelines.
• Experience deploying machine learning models into production environments.
• Experience monitoring production models and supporting services.
• Experience detecting and responding to model or data drift.
• Experience using version control for model code, datasets, configurations, pipelines, or deployment artifacts.
• Experience developing and executing rollback procedures for models, datasets, pipelines, or production configurations.
• Experience implementing automated testing for machine learning models, data pipelines, APIs, or deployment workflows.
• Experience with cloud-based machine learning services or production cloud environments.
• Understanding of model evaluation, validation metrics, production monitoring, model governance, and data governance.
• Ability to maintain traceability across datasets, transformations, model versions, evaluation results, deployments, and production outcomes.
• Ability to investigate and resolve model, dataset, pipeline, and deployment issues.
• Understanding of secure data pipelines, access controls, audit logging, encryption, secrets management, and dependency security.
• Ability to document and communicate model performance, drift, limitations, risks, incidents, and corrective actions.
• CompTIA Security+ CE or another approved IAT Level II certification.

Responsabilités

• Maintain training, validation, and testing datasets used for machine learning development and evaluation.
• Develop repeatable processes for dataset ingestion, preparation, transformation, validation, versioning, and controlled promotion.
• Evaluate datasets for quality, completeness, consistency, integrity, provenance, and fitness for their intended purpose.
• Build and maintain automated pipelines for data preparation, model training, model evaluation, packaging, deployment, monitoring, and rollback.
• Deploy machine learning models into approved cloud, application, analytical, and operational environments.
• Automate model build, test, validation, promotion, deployment, and recovery processes.
• Configure and maintain model registries and controlled repositories for models, datasets, code, configurations, dependencies, and evaluation results.
• Implement automated unit, integration, regression, data-quality, pipeline, and deployment testing.
• Establish model-evaluation procedures, performance measures, validation thresholds, and release-readiness criteria.
• Monitor production models for accuracy, performance, availability, latency, errors, data drift, concept drift, and unexpected behavior.
• Coordinate corrective actions with data, application, infrastructure, security, testing, and mission teams.
• Control updates to production models, datasets, pipelines, features, dependencies, and configurations through documented change and approval procedures.
• Maintain version control for model code, training logic, deployment scripts, datasets, features, configurations, and infrastructure definitions.
• Develop and test rollback procedures for model, dataset, pipeline, and configuration updates.
• Support secure data pipelines used for model training, evaluation, deployment, monitoring, and operational analysis.
• Maintain documentation covering model purpose, datasets, evaluation methods, performance, limitations, dependencies, deployments, changes, incidents, and known risks.
• Support the auditing and validation of model outputs to identify inaccurate, unsupported, or unexpected results.
• Help implement human-review and escalation checkpoints for model outputs that may affect mission or operational decisions.
• Collaborate with data engineering teams on data ingestion, transformation, metadata, warehouse integration, data quality, and governed access.
• Assist with architecture reviews, code reviews, release-readiness reviews, troubleshooting, operational handoff, and knowledge transfer.

Connaissances

ML Ops

Dataset maintenance

Model deployment

Production monitoring

Version control

Cloud platforms

Security/compliance

Troubleshooting

Documentation

Stakeholder communication

Formation

Bachelor's degree in CS/Engineering/Data Science

Outils

Docker

Kubernetes

MLflow

SageMaker

Terraform

Description du poste

DecisionPoint Corporation is seeking a Senior AI/ML & MLOps Engineer to build, deploy, monitor, maintain, and govern production ML capabilities for the USMEPCOM ENLIST program. The role focuses on training data, automated pipelines, model promotion, and secure governance across development, testing, and production environments.

The candidate will work with data scientists, cloud architects, cybersecurity personnel, testers, and government mission owners to ensure repeatable, traceable ML