AI/ML & MLOps Engineer (Senior)

Overview

DecisionPoint seeks a Senior AI/ML & MLOps Engineer to build, deploy, monitor, maintain, and govern production machine learning capabilities supporting the USMEPCOM ENLIST program.

This position will maintain training, validation, and testing datasets; develop automated model deployment pipelines; monitor deployed models; detect data and model drift; and control updates to production models, datasets, configurations, and supporting services. The engineer will implement repeatable, traceable, and secure processes for promoting machine learning capabilities from development through testing and into production.

The Senior AI/ML & MLOps Engineer will work with data scientists, data engineers, cloud architects, software developers, cybersecurity personnel, testers, operations teams, and Government mission owners. The successful candidate must be able to combine machine learning engineering with deployment automation, monitoring, version control, rollback planning, and model and data governance.

This position is remote, with Chicago-area candidates preferred.

Note: By applying to this position, you acknowledge and consent to having your resume included in an active competitive government contract bid.

Duties & Responsibilities

The Senior AI/ML & MLOps Engineer will:

• 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.

• Ensure datasets are maintained in or can be transformed into analysis-ready and model-ready formats.

• Evaluate datasets for quality, completeness, consistency, integrity, provenance, and fitness for their intended purpose.

• Maintain traceability among source data, transformed datasets, features, model versions, evaluation results, and production deployments.

• 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.

• Configure alerts, dashboards, thresholds, and escalation procedures for model degradation, drift, data-quality issues, and pipeline failures.

• Investigate production model, dataset, pipeline, and deployment anomalies.

• 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.

• Integrate MLOps workflows with continuous integration, continuous delivery, infrastructure-as-code, automated testing, monitoring, and incident-management capabilities.

• Support data and model governance requirements, including access controls, versioning, approval gates, traceability, change control, and audit logging.

• Coordinate with cybersecurity personnel to address vulnerabilities, identity and access controls, secrets management…