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.

Location: 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

Dataset Management

• 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

Model Deployment & Automation

• 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

Model Evaluation & Monitoring

• 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

Change & Version Control

• 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

Security & Governance

• Support secure data pipelines used for model training, evaluation, deployment, monitoring, and operational analysis
• 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, software dependencies, encryption, logging, and authorization requirements

Documentation & Communication

• 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
• 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

Collaboration & Integration

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

Required Qualifications

• 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

Preferred Qualifications

• 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

Core Skills

• Machine learning operations and lifecycle management
• Training, validation, and testing dataset maintenance
• Automated model deployment pipelines
• Production model monitoring
• Model and data drift detection
• Model evaluation and performance analysis
• Version control and configuration management
• Rollback and recovery planning
• Model and data governance
• Cloud ML platforms and secure data pipelines
• Automated testing and release validation
• Production troubleshooting and incident response
• Technical documentation and stakeholder communication
• Collaboration across data, software, cloud, security, testing, mission, and operations teams

Equal Employment Opportunity Policy

DecisionPoint Corporation is an Equal Employment Opportunity and Affirmative Action employer. It is the policy of DecisionPoint Corporation to provide equal employment opportunity in accordance with all applicable Equal Employment Opportunity/Affirmative Action laws, directives and regulations to all employees and qualified applicants without regard to race, ethnicity, color, religion, national origin, sex, age, disability status, pregnancy, sexual orientation, gender identity, genetic information, protected veteran status, or any other protected status under Federal, State or Local laws.

Pay Transparency Policy

In accordance with Presidential Executive Order 13665, DecisionPoint Corporation will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.

Authorization to Share Resume and Personal Information

By expressing your interest and submitting your resume for this position, you authorize DecisionPoint Corporation to share your resume, as well as personal information included on the resume, with its subsidiaries, affiliates and teaming partners for the purpose of considering you for this position and other available positions requiring comparable skills, education and experience. Should DecisionPoint Corporation or its affiliates and teaming partners wish to initiate pre-employment discussions, you will be asked to complete an employment application and related employment documents.