Senior AI/ML Engineer, MLOps

DecisionPoint Corporation

. Maintain training, validation, and testing datasets used for machine learning development and evaluation .

Posted 10/28/2026full-timeRemote • Illinois • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in maintaining and deploying machine learning models, including automated pipelines and version control. Proficient in model evaluation, monitoring, and governance, ensuring data integrity and compliance with security standards.

Highest-signal resume keywords

Machine Learning Model DeploymentAutomated Pipeline DevelopmentModel Monitoring and EvaluationVersion Control for Model CodeCompTIA Security+ CE Certification

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData PreparationModel Evaluation MetricsAutomated TestingData GovernanceVersion ControlRollback ProceduresCloud-Based Machine Learning ServicesData Drift DetectionChange Control Procedures

Soft Skills

Problem SolvingCommunicationCollaborationInvestigative SkillsDocumentation

Tools & Technologies

Automated Testing ToolsCloud EnvironmentsData PipelinesModel RegistriesMonitoring Dashboards

Certifications & Qualifications

CompTIA Security+ CE

Industry Keywords

MLOpsData QualityModel GovernanceAccess ControlsAudit Logging

Tech Stack

Tools & technologies

CloudCyber Security

About the role

Key responsibilities & impact

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 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 auditing and validation of model outputs to identify inaccurate, unsupported, or unexpected results
Help implement human-review and escalation checkpoints for model outputs affecting 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 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
Prepare model 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

Requirements

What you’ll need

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

Benefits

Comp & perks

Regular full-time employment
Opportunity to work remotely
Chicago-area candidates preferred
Opportunity to support a USMEPCOM ENLIST government program
Equal Employment Opportunity and Affirmative Action protections