Senior AI/ML Platform Engineer-Linthicum Heights, MD

Position Overview:
The Senior AI/ML Platform Engineer will design, implement, operate, and continuously improve the shared enterprise AI/ML platform. Key areas of focus include 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 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.
  – Hands-on experience with AWS, Azure, or GCP, including cloud resource management and cost optimization.
  – Advanced Kubernetes experience (cluster management, resource quotas, autoscaling, workload scheduling).
  – Experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
  – 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, rollbacks, 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 Qualifications:
  – Experience with shared platforms or Internal Developer Platforms (IDPs).
  – Familiarity with LLM serving technologies (e.g., vLLM, TGI, managed LLM APIs).
  – Background in government, regulated, or FedRAMP-compliant environments.
  – Knowledge of the NIST AI Risk Management Framework and responsible AI governance.
  – Familiarity with FinOps practices and tools (e.g., Infracost, AWS Cost Explorer).
  – Expertise in GitOps, platform APIs, developer tooling, and infrastructure observability.