AI/ML & MLOps Engineer (Senior)
DecisionPoint Technologies, Inc.2 years ago
Responsibilities
- Maintain training, validation, and testing datasets, including ingestion, transformation, quality evaluation, versioning, provenance, and controlled promotion.
- Build and maintain automated pipelines for data preparation, model training, evaluation, packaging, deployment, monitoring, testing, and rollback.
- Deploy machine learning models into approved cloud, application, analytical, and operational environments.
- Monitor production models for accuracy, performance, availability, latency, errors, data drift, concept drift, and unexpected behavior.
- Configure dashboards, alerts, thresholds, escalation procedures, and incident responses for model, data, and pipeline issues.
- Maintain model registries and controlled repositories for models, datasets, code, configurations, dependencies, and evaluation results.
- Implement model evaluation procedures, validation thresholds, release-readiness criteria, automated testing, and rollback procedures.
- Control production updates through documented change, testing, approval, version-control, traceability, and audit processes.
- Support secure data pipelines, access controls, encryption, secrets management, dependency security, logging, authorization, and governance requirements.
- Document model purposes, datasets, evaluations, limitations, deployments, changes, incidents, risks, monitoring plans, rollback procedures, and operating guides.
- Coordinate with cross-functional technical and Government stakeholders on architecture reviews, troubleshooting, operational handoff, corrective actions, and knowledge transfer.
- Communicate model performance, drift, risks, incidents, and corrective actions to technical teams, Government stakeholders, and program leadership.
Requirements
- Active Secret clearance or higher is required.
- Bachelor’s degree in computer science, data science, engineering, or a related field, or a permitted equivalent, is required.
- At least 10 years of relevant professional experience is required.
- Experience maintaining machine learning training, validation, and testing datasets is required.
- Experience building or maintaining automated ML deployment pipelines and deploying models into production is required.
- Experience monitoring production models and detecting and responding to model or data drift is required.
- Experience with version control, rollback procedures, documented change control, automated testing, cloud-based ML services, model evaluation, production monitoring, and model and data governance is required.
- Ability to maintain traceability across data, transformations, model versions, evaluations, deployments, and production outcomes is required.
- Understanding of secure data pipelines, access controls, audit logging, encryption, secrets management, and dependency security is required.
- CompTIA Security+ CE or another approved IAT Level II certification is required.
- Preferred qualifications include AWS Certified Machine Learning Engineer - Associate or comparable certification, additional AWS, Microsoft Azure, Google Cloud, Databricks, data engineering, or cloud architecture certification, and federal or Department of Defense experience.
- Preferred experience includes MLflow, Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, Kubeflow, model registries, feature stores, metadata repositories, experiment tracking, data lineage, Docker, Kubernetes, infrastructure-as-code, observability, incident response, responsible AI, explainability, bias evaluation, human oversight, generative AI, retrieval workflows, or agentic AI capabilities.
Benefits
- Remote position; Chicago-area candidates are preferred.
- Applicants consent to having their resumes included in an active competitive government contract bid and to sharing resume information with subsidiaries, affiliates, and teaming partners for consideration.
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.