AI/ML & MLOps Engineer (SME)
DecisionPoint Technologies, Inc.2 years ago
Responsibilities
- Lead the design and implementation of secure, scalable, supportable production machine learning architectures.
- Develop, govern, version, validate, and maintain training, validation, and testing datasets and their associated lineage, provenance, quality, and authorized-use controls.
- Design and automate ML pipelines for data preparation, training, evaluation, packaging, deployment, monitoring, retraining, rollback, recovery, and retirement.
- Deploy models into approved cloud, application, analytical, and operational environments and establish controlled promotion, approval, and release processes.
- Implement model registries and controlled repositories for models, datasets, code, configurations, dependencies, evaluation results, and approvals.
- Develop automated tests and define model evaluation methods, performance thresholds, validation criteria, and release-readiness requirements.
- Monitor model performance, drift, inference quality, latency, availability, errors, resource utilization, and operational health, and coordinate corrective actions.
- Implement governance and security controls covering version control, environment separation, approval gates, auditability, change management, access controls, secrets, logging, encryption, and authorization.
- Maintain traceability and audit-ready documentation across source data, transformations, model versions, evaluations, deployments, outcomes, limitations, risks, and changes.
- Integrate ML workflows with continuous integration, continuous delivery, automated testing, infrastructure-as-code, monitoring, and incident-management capabilities.
- Provide technical leadership, architecture guidance, code and design reviews, troubleshooting, knowledge transfer, technical reports, and communication to government stakeholders and program leadership.
Requirements
- Active Secret clearance or higher.
- Bachelor’s degree in computer science, data science, mathematics, engineering, or a related field, or a permitted equivalent.
- At least 12 years of relevant professional experience.
- Experience leading production machine learning architecture and operating ML or MLOps environments.
- Experience managing training, validation, and testing datasets and establishing model evaluation methods, performance measures, validation criteria, and acceptance thresholds.
- Experience deploying ML models to production and automating model build, testing, deployment, promotion, rollback, or retraining.
- Experience implementing model and data drift monitoring, model and data governance, controlled updates, and artifact/configuration versioning.
- Experience with cloud-based ML platforms or production cloud architectures and automated testing, continuous integration, and continuous delivery for ML 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, evaluations, deployments, and operational outcomes.
- CompTIA Security+ CE or another approved IAT Level II certification.
- Preferred: AWS Certified Machine Learning Engineer - Associate or comparable ML platform certification; advanced AWS, Microsoft Azure, Google Cloud, Databricks, or comparable cloud architecture/data engineering certification.
- Preferred: Federal or Department of Defense experience; experience in regulated, controlled, or mission-critical environments; and experience with MLflow, Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, Kubeflow, containerization, Kubernetes, infrastructure as code, feature stores, model registries, observability, responsible AI, generative AI, retrieval workflows, or agentic AI systems.
- Preferred: Experience leading technical teams, architecture reviews, or cross-functional ML delivery initiatives.
Benefits
- Remote position; Chicago-area candidates are preferred.
- The role supports an active competitive government contract bid, and applicants consent to having their resumes included in that bid.
- Equal Employment Opportunity and Affirmative Action employer.
Tech Stack
Categories
About DecisionPoint Technologies, Inc.
DecisionPoint Technologies, Inc. provides enterprise mobility and edge technology integration for large organizations, combining its IntelliTrack asset and inventory platform with managed services such as device lifecycle management, RFID, and help desk support. The company sells hardware, software, and implementation/support services to enterprises in supply chain, retail, and government. Headquartered in Baltimore, Maryland, it operates across the U.S. and Canada and is privately held.