AI/ML Engineering Lead

Skills

• Ansible
• Cloud Computing
• FedRAMP
• IDPS
• Machine Learning Operations (ML Ops)
• Machine Learning (ML)
• Ansible
• Cloud Computing
• FedRAMP
• IDPS
• Machine Learning Operations (ML Ops)
• Machine Learning (ML
• -

SummaryPosition Overview:

The Senior AI/ML Platform Engineer will design, implement, operate, and continuously improve the shared enterprise AI/ML platform. The role focuses on platform automation, MLOps, CI/CD, cloud infrastructure, observability, reliability engineering, and cost optimization.

Key Requirements:

• 10+ years of experience in platform engineering, MLOps, or data/software engineering, including 2+ years working with production ML systems.
• Bachelor's degree in Computer Science, Information Systems, Engineering, Business, or a related technical discipline; master's degree preferred.
• Strong proficiency in Python and production-grade software development.
• Experience designing and improving

ML CI/CD

pipelines, automation frameworks, and deployment processes.

• Experience with AWS, Azure, or Google Cloud Platform, including cloud resource management and cost optimization.
• Advanced Kubernetes experience, including cluster management, resource quotas, autoscaling, and workload scheduling.
• Experience with Prometheus, Grafana, Datadog, OpenTelemetry, or equivalent observability tools.
• Production-scale experience with Terraform and Ansible.
• Strong understanding of the ML lifecycle, experiment tracking, model registries, feature stores, model serving, and drift monitoring.
• Experience with automated model validation, testing, A/B testing, canary deployments, rollback, and production releases.
• Experience developing internal SDKs, CLI tools, reusable templates, APIs, and standardized deployment workflows.
• Strong troubleshooting, incident response, root-cause analysis, and technical documentation skills.

Preferred Experience:

• Shared platforms or internal developer platforms (IDPs).
• LLM serving technologies such as vLLM, Text Generation Inference (TGI), and managed LLM APIs.
• Government, regulated, or FedRAMP-compliant environments.
• NIST AI Risk Management Framework and responsible AI governance.
• FinOps practices and tools such as Infracost and AWS Cost Explorer.
• GitOps, platform APIs, developer tooling, and infrastructure observability.

Employers have access to artificial intelligence language tools ("AI") that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.