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# Senior AI/ML Platform Engineer

- **Company:** Metalight Solutions Inc
- **Location:** Linthicum Heights, MD, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Information Systems, Continuous Integration, Information Engineering, Programming Tools, Monitoring of Systems, Python (Programming Language), Machine Learning, Release Management, Ansible, Prometheus, Azure Machine Learning, Software Deployment, Software Engineering, Datadog, Data Logging, Feature Store, Google Cloud, Autoscaling, System Availability, Large Language Models, Grafana, Core Api, Git Flow, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Deployment Automation, Machine Learning Operations, Terraform, Natural Language Generation
- **Published:** October 27, 2026
- **Apply:** https://www.dice.com/job-detail/a29bc544-9ae9-42f7-902a-4e4b5f0e16a9

## About the Role

We are looking for a highly experienced Senior AI/ML Platform Engineer to design, build, maintain, and optimize enterprise AI/ML platforms. The ideal candidate will have strong hands-on experience in platform engineering, MLOps, cloud infrastructure, Kubernetes, CI/CD automation, and production machine learning systems., * 10+ years of experience in platform engineering, MLOps, data engineering, or software engineering.

 * At least 2 years of hands-on experience supporting production machine learning systems.
 * Strong Python programming and production software development skills.
 * Experience building ML CI/CD pipelines and automated deployment frameworks.
 * Hands-on experience with AWS, Azure, or Google Cloud Platform.
 * Advanced Kubernetes and container orchestration experience.
 * Production-scale experience with Terraform and Ansible.
 * Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
 * Strong understanding of MLOps, model deployment, model monitoring, and the machine learning lifecycle.
 * Excellent troubleshooting, incident management, documentation, and communication skills.
 * Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline; a master's degree is preferred., * Experience building shared enterprise platforms or Internal Developer Platforms (IDPs).
 * Experience with LLM serving technologies such as vLLM, Text Generation Inference (TGI), or managed LLM APIs.
 * Experience in government, regulated, or FedRAMP-compliant environments.
 * Familiarity with the NIST AI Risk Management Framework and responsible AI governance.
 * Knowledge of FinOps tools and practices, including Infracost and AWS Cost Explorer.
 * Experience with GitOps, platform APIs, developer tooling, and infrastructure observability.

## Description

This role focuses on building scalable, reliable, secure, and cost-efficient platforms that help data scientists and ML engineers deploy, monitor, and manage machine learning models in production., * Design, build, and maintain enterprise AI/ML platforms and production MLOps infrastructure.

 * Develop and optimize ML CI/CD pipelines, deployment automation, and infrastructure provisioning.
 * Manage cloud infrastructure across AWS, Azure, or Google Cloud Platform, with a focus on reliability, scalability, and cost optimization.
 * Manage Kubernetes clusters, resource allocation, autoscaling, workload scheduling, and production deployments.
 * Implement infrastructure automation using Terraform and Ansible.
 * Build monitoring, logging, and observability solutions using Prometheus, Grafana, Datadog, OpenTelemetry, or similar tools.
 * Support the complete ML lifecycle, including experiment tracking, model registries, feature stores, model serving, and model drift monitoring.
 * Implement automated model testing, validation, canary releases, A/B testing, rollback, and production release strategies.
 * Develop reusable SDKs, CLI tools, APIs, deployment templates, and standardized workflows for engineering teams.
 * Troubleshoot production incidents, perform root-cause analysis, and improve platform reliability.
 * Apply security, governance, responsible AI, and FinOps practices where required.