AI/ML & MLOps Engineer (Senior)

DecisionPoint | Cortek•Chicago, IL

1d•Remote

About The Position

DecisionPoint seeks a Senior AI/ML & MLOps Engineer to build, deploy, monitor, maintain, and govern production machine learning capabilities supporting the USMEPCOM ENLIST program. The role maintains training, validation, and testing datasets; develops automated model deployment pipelines; monitors production models and detects data and model drift; and controls updates to production models, datasets, configurations, and supporting services. It establishes repeatable, traceable, and secure processes for promoting machine learning capabilities from development through testing and into production. The engineer works with data scientists, data engineers, cloud architects, software developers, cybersecurity personnel, testers, operations teams, and Government mission owners. The position combines machine learning engineering with deployment automation, monitoring, version control, rollback planning, and model and data governance. The role is remote, with Chicago-area candidates preferred. Applicants acknowledge and consent to having their resume included in an active competitive government contract bid.

Requirements

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 controlling model and dataset updates through documented change, testing, and approval procedures.
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.

Nice To Haves

AWS Certified Machine Learning Engineer - Associate or a comparable machine learning platform certification.
Additional AWS, Microsoft Azure, Google Cloud, Databricks, data engineering, or cloud architecture certification.
Experience supporting federal or Department of Defense programs.
Experience operating machine learning capabilities in secure, regulated, or mission-critical environments.
Experience with MLflow, Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, Kubeflow, or comparable platforms.
Experience with model registries, feature stores, metadata repositories, experiment tracking, or data-lineage tools.
Experience with Docker, Kubernetes, infrastructure as code, and cloud-native deployment patterns.
Experience implementing observability, automated alerting, incident response, and production reliability controls.
Familiarity with responsible AI practices, model explainability, output validation, bias evaluation, and human oversight.
Familiarity with DoD Impact Level 4 or comparable Government cloud and data-protection requirements.
Experience supporting analytical models, generative AI, retrieval workflows, or agentic AI capabilities.

Responsibilities

Maintain training, validation, and testing datasets for machine learning development and evaluation.
Develop repeatable processes for dataset ingestion, preparation, transformation, validation, versioning, and controlled promotion.
Ensure datasets are analysis-ready and model-ready, and evaluate their quality, completeness, consistency, integrity, provenance, and fitness for 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 and 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, and coordinate corrective actions with relevant technical and mission teams.
Control production updates to 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 for model training, evaluation, deployment, monitoring, and operational analysis.
Document model purpose, datasets, evaluation methods, performance, limitations, dependencies, deployments, changes, incidents, and known risks.
Support auditing and validation of model outputs to identify inaccurate, unsupported, or unexpected results.
Help implement human-review and escalation checkpoints for 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 on vulnerabilities, identity and access controls, secrets management, software dependencies, encryption, logging, and authorization requirements.
Collaborate with data engineering teams on ingestion, transformation, metadata, warehouse integration, data quality, and governed access.
Assist with architecture and code reviews, release-readiness reviews, troubleshooting, operational handoff, and knowledge transfer.
Prepare lifecycle documentation, deployment instructions, rollback procedures, monitoring plans, test results, technical diagrams, and operating guides.
Communicate model status, performance, drift, risks, incidents, and corrective actions to technical teams, Government stakeholders, and program leadership.