{"id":3416240,"url":"https://alion.io/job/decisionpoint-corporation-aiml-mlops-engineer-sme","title":"AI/ML & MLOps Engineer (SME)","company":{"id":3863688,"name":"DecisionPoint Corporation","domain":"decisionpointcorp.com","url":"https://alion.io/company/decisionpoint-corporation","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"iCIMS","truth_index":{"grade":"A","score":100,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":26,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chicago, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":132000,"max_usd":249000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":197},"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Machine Learning","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Databricks","optional":true},{"name":"Feature Store","optional":true},{"name":"GCP","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-28T19:31:47Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-03T04:46:00Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Overview\nDecisionPoint seeks an AI/ML &MLOpsEngineer to lead the architecture, deployment, monitoring, automation, and governance of production machine learning capabilities supporting the USMEPCOM ENLIST program.\nThis SME-level position will maintain training, validation, and testing datasets; design and operate production ML architectures; automate model deployment; monitor model performance and drift; and control updates to models, datasets, pipelines, and supporting configurations. The engineer will establish reproducible, traceable, and secure processes for moving machine learning capabilities from development through validation and into production.\nThe AI/ML & MLOps Engineer will work closely with mission owners, data scientists, data engineers, cloud architects, software developers, cybersecurity personnel, testers, and operations teams. The successful candidate must combine advanced machine learning engineering knowledge with production automation, secure cloud architecture, data governance, and operational monitoring expertise.\nThis position is remote, with Chicago-area candidates preferred.\nNote: By applying to this position, you acknowledge and consent to having your resume included in an active competitive government contract bid. \nDuties & Responsibilities\nThe AI/ML & MLOps Engineer will:\nLead the design and implementation of secure, scalable, and supportable production machine learning architectures. \nDevelop, maintain, and govern training, validation, and testing datasets used for model development and evaluation. \nEstablish repeatable processes for dataset ingestion, preparation, transformation, labeling, validation, versioning, and approval. \nEnsure datasets are maintained in or transformed into analysis-ready and model-ready formats. \nImplement controls for dataset quality, completeness, integrity, provenance, lineage, security, and authorized use. \nDesign and automate end-to-end ML pipelines covering data preparation, training, evaluation, packaging, deployment, monitoring, retraining, and retirement. \nDeploy machine learning models into approved cloud, application, analytical, and operational environments. \nEstablish automated model build, test, validation, promotion, deployment, rollback, and recovery procedures. \nImplement model registries and controlled repositories for model artifacts, dataset versions, code, configurations, dependencies, evaluation results, and approvals. \nDevelop automated tests for model pipelines, data transformations, interfaces, deployment packages, and production behavior. \nDefine model evaluation frameworks, performance thresholds, validation criteria, and release-readiness requirements. \nEvaluate model accuracy, reliability, robustness, performance, scalability, security, and alignment with documented mission requirements. \nImplement production monitoring for model performance, data drift, concept drift, inference quality, latency, availability, errors, and resource utilization. \nEstablish thresholds, alerts, escalation paths, and response procedures for model degradation, drift, data-quality issues, and operational failures. \nInvestigate model and pipeline anomalies and coordinate corrective actions with data, application, infrastructure, security, and mission teams. \nControl updates to production models, datasets, features, pipelines, configurations, and dependencies through documented review and approval processes. \nMaintain traceability among source datasets, data transformations, model versions, evaluation results, deployment records, and production outcomes. \nDevelop rollback and recovery procedures for models, datasets, pipelines, and supporting services. \nSupport secure data pipelines used for AI development, model evaluation, deployment, monitoring, and operational decision support. \nImplement technical controls supporting model and data governance, including version control, separation of environments, approval gates, auditability, and change management. \nMaintain audit-ready documentation covering model purpose, training data, evaluation methods, limitations, dependencies, approvals, changes, performance, and known risks. \nSupport the evaluation and auditing of model outputs to identify inaccurate, unsupported, or unexpected results. \nHelp establish human-review checkpoints and escalation procedures for model outputs that could materially affect mission or operational decisions. \nSupport compliance with applicable Government data-handling, cloud-security, cybersecurity, and operational-risk requirements. \nIntegrate model development and deployment workflows with continuous integration, continuous delivery, automated testing, infrastructure-as-code, monitoring, and incident-management capabilities. \nPartner with cybersecurity teams to address vulnerabilities, access controls, secrets management, software dependencies, logging, encryption, and authorization requirements. \nCoordinate with data warehouse and data engineering teams on data models, metadata, Extract-Transform-Load processes, data quality, and governed access. \nProvide technical leadership, architecture guidance, code and design reviews, troubleshooting support, and knowledge transfer. \nPrepare architecture diagrams, model lifecycle documentation, deployment procedures, monitoring plans, test results, operating guides, and technical reports. \nCommunicate model performance, drift, risks, incidents, corrective actions, and deployment status to technical teams, Government stakeholders, and program leadership.\nQualifications\nRequired Qualifications\nCandidates must meet all the following requirements:\nActive Secret clearance or higher. \nBachelor’s degree in computer science, data science, mathematics, engineering, or a related field, or a permitted equivalent. \nMinimum of 12 years of relevant professional experience. \nExperience leading the architecture of production machine learning systems. \nExperience designing and operating production ML or MLOps environments. \nExperience developing and maintaining training, validation, and testing datasets. \nExperience establishing model-evaluation methods, performance measures, validation criteria, and acceptance thresholds. \nExperience deploying machine learning models into production environments. \nExperience automating model build, test, deployment, promotion, rollback, or retraining processes. \nExperience implementing model and data drift monitoring. \nExperience with model governance and data governance. \nExperience controlling model, dataset, pipeline, and configuration updates. \nExperience with cloud-based machine learning platforms or production cloud architectures. \nExperience implementing model, dataset, code, artifact, and configuration versioning. \nExperience building or integrating automated testing, continuous integration, and continuous delivery capabilities for machine learning systems. \nExperience monitoring production model performance, reliability, and operational health. \nUnderstanding of secure data pipelines, access controls, audit logging, encryption, secrets management, and software supply-chain security. \nAbility to establish traceability across datasets, transformations, model versions, evaluation results, deployments, and operational outcomes. \nAbility to document and communicate model limitations, performance, drift, risks, incidents, and corrective actions. \nCompTIA Security+ CE or another approved IAT Level II certification. \nPreferred Qualifications\nThe following qualifications are preferred but are not required:\nAWS Certified Machine Learning Engineer - Associate or a comparable machine learning platform certification. \nAdvanced AWS, Microsoft Azure, Google Cloud, Databricks, or comparable cloud architecture or data engineering certification. \nExperience supporting federal or Department of Defense programs. \nExperience operating machine learning capabilities in regulated, controlled, or mission-critical environments. \nExperience with MLflow, Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, Kubeflow, or comparable platforms. \nExperience with containerization, Kubernetes, infrastructure as code, and cloud-native deployment patterns. \nExperience implementing feature stores, model registries, metadata repositories, experiment tracking, or data-lineage tools. \nExperience with observability platforms, automated alerting, incident response, and production reliability engineering. \nFamiliarity with responsible AI, explainability, output validation, human oversight, bias evaluation, and model-risk management practices. \nFamiliarity with DoD Impact Level 4 or comparable Government cloud and data-protection requirements. \nExperience with analytical models, generative AI, retrieval workflows, or agentic AI systems. \nExperience leading technical teams, architecture reviews, or cross-functional ML delivery initiatives. \nSkills\nProduction machine learning architecture. \nMLOps engineering and lifecycle automation. \nModel deployment and serving. \nTraining, validation, and testing dataset management. \nModel evaluation and performance measurement. \nModel and data drift monitoring. \nModel, data, and configuration governance. \nAutomated testing and deployment pipelines. \nCloud ML platforms and secure data pipelines. \nVersion control, traceability, and audit documentation. \nProduction monitoring and incident troubleshooting. \nCybersecurity and access-control implementation. \nTechnical leadership and architecture communication. \nCross-functional collaboration with mission, data, software, cloud, security, testing, and operations teams. \nOur Equal Employment Opportunity Policy\nEEO and Affirmative Action 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.\n 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.\n 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. 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