Senior AI/ML Engineer, MLOps

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🔥 0 minutes ago

🌽 Illinois – Remote

🌽 Illinois – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 25%

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DecisionPoint Corporation

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2011

🎖️ Defense

💼 Consulting

📦 Logistics

Defense • Consulting • Logistics

DecisionPoint Corporation is a Service Disabled Veteran Owned Small Business specializing in IT modernization and digital transformation for government agencies. They provide comprehensive services including cybersecurity, health IT, and intelligent automation solutions, aimed at improving operational efficiency and effectiveness while ensuring secure cloud migration. Their Centers of Excellence approach drives mission success by addressing the most pressing challenges faced by government entities through innovative technology and expert support.

📋 Description

• 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

• 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

• 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