AI/ML & MLOps Engineer (SME)

Overview

DecisionPoint seeks an AI/ML & MLOps Engineer to lead the architecture, deployment, monitoring, automation, and governance of production machine learning capabilities supporting the USMEPCOM ENLIST program.

This SME-level position will maintain training, validation, and testing datasets; design and operate production ML architectures; automate model deployment; monitor model performance and drift; and control updates to models, datasets, pipelines, and supporting configurations. The engineer will establish reproducible, traceable, and secure processes for moving machine learning capabilities from development through validation and into production.

The AI/ML & MLOps Engineer will work closely with mission owners, data scientists, data engineers, cloud architects, software developers, cybersecurity personnel, testers, and operations teams. The successful candidate must combine advanced machine learning engineering knowledge with production automation, secure cloud architecture, data governance, and operational monitoring expertise.

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 AI/ML & MLOps Engineer will:

• Lead the design and implementation of secure, scalable, and supportable production machine learning architectures.

• Develop, maintain, and govern training, validation, and testing datasets used for model development and evaluation.

• Establish repeatable processes for dataset ingestion, preparation, transformation, labeling, validation, versioning, and approval.

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

• Implement controls for dataset quality, completeness, integrity, provenance, lineage, security, and authorized use.

• Design and automate end-to-end ML pipelines covering data preparation, training, evaluation, packaging, deployment, monitoring, retraining, and retirement.

• Deploy machine learning models into approved cloud, application, analytical, and operational environments.

• Establish automated model build, test, validation, promotion, deployment, rollback, and recovery procedures.

• Implement model registries and controlled repositories for model artifacts, dataset versions, code, configurations, dependencies, evaluation results, and approvals.

• Develop automated tests for model pipelines, data transformations, interfaces, deployment packages, and production behavior.