AI/ML, MLOps Engineer

DecisionPoint Corporation

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

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing secure, scalable machine learning architectures, with a strong focus on model governance, dataset management, and production monitoring. Proficient in deploying machine learning models in cloud environments and establishing automated processes for model lifecycle management.

Highest-signal resume keywords

Machine Learning Architecture DesignModel Governance and Data GovernanceCloud-Based Machine Learning PlatformsAutomated Testing and Continuous IntegrationProduction Model Monitoring and Performance Evaluation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDataset ManagementModel Evaluation FrameworksAutomated ML PipelinesData Drift MonitoringVersion ControlCloud ArchitectureInfrastructure as CodeContainerizationData Lineage Tools

Soft Skills

Technical LeadershipCommunicationProblem-SolvingCollaborationDocumentation

Tools & Technologies

AWSAzureGoogle CloudMLflowAmazon SageMakerDatabricksKubeflowObservability PlatformsIncident Management ToolsAutomated Alerting Systems

Certifications & Qualifications

Active Secret ClearanceCompTIA Security+ CEAWS Certified Machine Learning Engineer - Associate

Industry Keywords

MLOpsRegulated EnvironmentsFederal ProgramsDoD ComplianceResponsible AI

Tech Stack

Tools & technologies

AWSAzureCloudCyber SecurityETLKubernetes

About the role

Key responsibilities & impact

Lead the design and implementation of secure, scalable, and supportable production machine learning architectures
Develop, maintain, and govern training, validation, and testing datasets
Establish repeatable dataset ingestion, preparation, transformation, labeling, validation, versioning, and approval processes
Implement controls for dataset quality, completeness, integrity, provenance, lineage, security, and authorized use
Design and automate end-to-end ML pipelines covering 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 artifacts, dataset versions, code, configurations, dependencies, evaluation results, and approvals
Develop automated tests for model pipelines, data transformations, interfaces, deployment packages, and production behavior
Define model evaluation frameworks, performance thresholds, validation criteria, and release-readiness requirements
Evaluate model accuracy, reliability, robustness, performance, scalability, security, and alignment with mission requirements
Implement production monitoring for model performance, data drift, concept drift, inference quality, latency, availability, errors, and resource utilization
Establish thresholds, alerts, escalation paths, and response procedures for degradation, drift, data-quality issues, and operational failures
Investigate model and pipeline anomalies and coordinate corrective actions with data, application, infrastructure, security, and mission teams
Control updates to production models, datasets, features, pipelines, configurations, and dependencies through documented review and approval processes
Maintain traceability among source datasets, transformations, model versions, evaluation results, deployment records, and production outcomes
Develop rollback and recovery procedures for models, datasets, pipelines, and supporting services
Support secure data pipelines for AI development, model evaluation, deployment, monitoring, and operational decision support
Implement model and data governance controls, including version control, environment separation, approval gates, auditability, and change management
Maintain audit-ready documentation covering model purpose, training data, evaluation methods, limitations, dependencies, approvals, changes, performance, and known risks
Support evaluation and auditing of model outputs to identify inaccurate, unsupported, or unexpected results
Help establish human-review checkpoints and escalation procedures for outputs that could materially affect mission or operational decisions
Support compliance with Government data-handling, cloud-security, cybersecurity, and operational-risk requirements
Integrate workflows with continuous integration, continuous delivery, automated testing, infrastructure-as-code, monitoring, and incident-management capabilities
Partner with cybersecurity teams on vulnerabilities, access controls, secrets management, software dependencies, logging, encryption, and authorization
Coordinate with data warehouse and data engineering teams on data models, metadata, ETL processes, data quality, and governed access
Provide technical leadership, architecture guidance, code and design reviews, troubleshooting support, and knowledge transfer
Prepare architecture diagrams, lifecycle documentation, deployment procedures, monitoring plans, test results, operating guides, and technical reports
Communicate model performance, drift, risks, incidents, corrective actions, and deployment status 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, mathematics, engineering, or a related field, or a permitted equivalent
Minimum of 12 years of relevant professional experience
Experience leading the architecture of production machine learning systems
Experience designing and operating production ML or MLOps environments
Experience developing and maintaining training, validation, and testing datasets
Experience establishing model-evaluation methods, performance measures, validation criteria, and acceptance thresholds
Experience deploying machine learning models into production environments
Experience automating model build, test, deployment, promotion, rollback, or retraining processes
Experience implementing model and data drift monitoring
Experience with model governance and data governance
Experience controlling model, dataset, pipeline, and configuration updates
Experience with cloud-based machine learning platforms or production cloud architectures
Experience implementing model, dataset, code, artifact, and configuration versioning
Experience building or integrating automated testing, continuous integration, and continuous delivery capabilities for machine learning systems
Experience monitoring production model performance, reliability, and operational health
Understanding of secure data pipelines, access controls, audit logging, encryption, secrets management, and software supply-chain security
Ability to establish traceability across datasets, transformations, model versions, evaluation results, deployments, and operational outcomes
Ability to document and communicate model limitations, performance, drift, risks, incidents, and corrective actions
CompTIA Security+ CE or another approved IAT Level II certification
Preferred: AWS Certified Machine Learning Engineer - Associate or comparable machine learning platform certification
Preferred: Advanced AWS, Microsoft Azure, Google Cloud, Databricks, or comparable cloud architecture or data engineering certification
Preferred: Experience supporting federal or Department of Defense programs
Preferred: Experience operating machine learning capabilities in regulated, controlled, or mission-critical environments
Preferred: Experience with MLflow, Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, Kubeflow, or comparable platforms
Preferred: Experience with containerization, Kubernetes, infrastructure as code, and cloud-native deployment patterns
Preferred: Experience implementing feature stores, model registries, metadata repositories, experiment tracking, or data-lineage tools
Preferred: Experience with observability platforms, automated alerting, incident response, and production reliability engineering
Preferred: Familiarity with responsible AI, explainability, output validation, human oversight, bias evaluation, and model-risk management practices
Preferred: Familiarity with DoD Impact Level 4 or comparable Government cloud and data-protection requirements
Preferred: Experience with analytical models, generative AI, retrieval workflows, or agentic AI systems
Preferred: Experience leading technical teams, architecture reviews, or cross-functional ML delivery initiatives

Benefits

Comp & perks

Equal Employment Opportunity and Affirmative Action employer
Pay transparency protections
Opportunity to be considered for other available positions requiring comparable skills, education, and experience