AI/ML & MLOps Engineer (SME)

Careers Decisionpointcorp, Chicago, IL

Posted pay

Pay not posted

Location: Chicago, IL
Work type: Remote
Level: Not given
Experience: 12+ years
Posted: Nov 10, 2024
Last check: 31 h ago
Job type: ML / AI engineering, Inference & ML systems

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The role

Overview

DecisionPoint seeks an AI/ML & MLOps Engineer to lead the architecture, deployment, monitoring, automation, and governance of production machine learning capabilities supporting the USMEPCOM ENLIST program.

What you need

Qualifications

Required Qualifications

Candidates must meet all the following requirements

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.

Skills

Production machine learning architecture.
MLOps engineering and lifecycle automation.
Model deployment and serving.
Training, validation, and testing dataset management.
Model evaluation and performance measurement.
Model and data drift monitoring.
Model, data, and configuration governance.
Automated testing and deployment pipelines.
Cloud ML platforms and secure data pipelines.
Version control, traceability, and audit documentation.
Production monitoring and incident troubleshooting.
Cybersecurity and access-control implementation.
Technical leadership and architecture communication.
Cross-functional collaboration with mission, data, software, cloud, security, testing, and operations teams.

The AI/ML & MLOps Engineer will

Lead the design and implementation of secure, scalable, and supportable production machine learning architectures.
Develop, maintain, and govern training, validation, and testing datasets used for model development and evaluation.
Establish repeatable processes for dataset ingestion, preparation, transformation, labeling, validation, versioning, and approval.
Ensure datasets are maintained in or transformed into analysis-ready and model-ready formats.
Implement controls for dataset quality, completeness, integrity, provenance, lineage, security, and authorized use.
Design and automate end-to-end ML pipelines covering data 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 model 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 documented 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 model 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, data 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 used for AI development, model evaluation, deployment, monitoring, and operational decision support.
Implement technical controls supporting model and data governance, including version control, separation of environments, 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 the evaluation and auditing of model outputs to identify inaccurate, unsupported, or unexpected results.
Help establish human-review checkpoints and escalation procedures for model outputs that could materially affect mission or operational decisions.
Support compliance with applicable Government data-handling, cloud-security, cybersecurity, and operational-risk requirements.
Integrate model development and deployment workflows with continuous integration, continuous delivery, automated testing, infrastructure-as-code, monitoring, and incident-management capabilities.
Partner with cybersecurity teams to address vulnerabilities, access controls, secrets management, software dependencies, logging, encryption, and authorization requirements.
Coordinate with data warehouse and data engineering teams on data models, metadata, Extract-Transform-Load processes, data quality, and governed access.
Provide technical leadership, architecture guidance, code and design reviews, troubleshooting support, and knowledge transfer.
Prepare architecture diagrams, model 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.

Our Equal Employment Opportunity Policy

EEO 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.
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.

AI/ML & MLOps Engineer (SME)

The role

AI/ML & MLOps Engineer (SME)

The role

What you need

Qualifications

Candidates must meet all the following requirements

Skills

The AI/ML & MLOps Engineer will

The following qualifications are preferred but are not required

Our Equal Employment Opportunity Policy

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