Site Reliability Engineer at Thales managing secure cloud environments on AWS and GCP. Ensuring compliance, security, and availability of critical cloud platforms with DevSecOps practices.
About the role
- Lead DevOps Engineer designing cloud infrastructure for ML/AI solutions in medical imaging. Collaborating across teams for scalable, secure platforms that optimize data operations.
Responsibilities
- 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.
Benefits
- medical, dental, vision
- paid time off
- 401(k) plan with employee and company contribution opportunities
- life, disability, and accident insurance
- tuition reimbursement
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