Lead DevOps Engineer – GCP, AWS – ML/AI for Medical Imaging

Role Overview

• Partner with ML research, data engineering, and application teams to translate requirements into reliable, secure, and cost-effective platform capabilities.
• Lead design reviews, RFCs, and proof-of-concepts; mentor team members on cloud, Kubernetes, and data best practices.
• Own incident response for platform components and drive continuous improvement through automation and standards.
• Design and implement secure, scalable, multi-cloud (GCP + AWS) configurations.
• Establish and maintain infrastructure as code (IaC) standards with Terraform.
• Lead cloud-to-cloud data migration including secure transfer planning, checksum/manifest validation, parallelization, and cutover strategy.
• Implement robust ingestion pipelines for medical images and metadata into structured data stores with schema management, versioning, and data lineage.
• Optimize storage tiers and caching strategies for high-throughput image workloads.
• Establish cost observability with budgets, alerts, showback/chargeback, and automated idle resource cleanup.
• Own permissions and access management across clouds.
• Plan and execute winddown and exit from prior cloud providers: data egress, dependency mapping, app cutover, contract/savings plan termination, and archival with retention policies.
• Stand up and maintain managed ML platforms (Vertex AI) or managed Kubernetes clusters (GKE/EKS) with CI/CD for pipelines, images, and deployments.
• Partner with data/ML teams to codify data management practices: versioned datasets, reproducible preprocessing, clear lineage, and documentation.

Requirements

• 7+ years in DevOps/SRE/Platform roles, including multi-cloud (AWS/Azure/GCP) experience
• Deep proficiency with Terraform, CI/CD (GitHub Actions/GitLab/CodeBuild/Cloud Build), and Kubernetes (EKS/GKE)
• Hands-on experience with GPU workloads for ML training/inference and object storage patterns for large image datasets
• Proven track record in data migration (cloud-to-cloud), structured data ingestion (e.g., BigQuery/Redshift/Postgres), and schema/governance
• Strong security mindset: IAM, secrets, KMS, network isolation, private endpoints, encryption, auditability
• Demonstrated cost optimization (FinOps) across compute/storage/networking with measurable savings
• Excellent cross-functional communication; ability to lead architectural direction and mentor engineers.

Tech Stack

• Amazon Redshift
• AWS
• Azure
• BigQuery
• Cloud
• Google Cloud Platform
• Kubernetes
• Postgres
• Terraform

Benefits

• medical, dental, vision
• paid time off
• 401(k) plan with employee and company contribution opportunities
• life, disability, and accident insurance
• tuition reimbursement