Lead the design, build, and operation of cloud infrastructure supporting ML experimentation, training, and production deployments
Define technical direction and best practices for ML platforms, MLOps, reliability, and cloud infrastructure
Architect ML platforms for high availability, fault tolerance, and resiliency across supported environments
Build and oversee CI/CD pipelines and automation for infrastructure and ML workflows
Champion MLOps best practices including model versioning, validation, promotion, monitoring, and rollback strategies
Mentor engineers through design reviews, code reviews, and hands-on technical leadership

Requirements

Proven experience leading cloud platform or infrastructure initiatives
Strong hands-on experience with cloud platforms (Azure, AWS, and/or GCP)
Deep knowledge of infrastructure as code, automation, CI/CD, and reliability engineering
Experience designing highly available and resilient distributed systems
Experience with ML platforms or MLOps tooling (e.g., MLflow, Kubeflow, Azure ML, SageMaker, Vertex AI)
Familiarity with observability tools (e.g., Datadog, ELK, New Relic, Prometheus)
Strong communication skills and a leadership mindset
6 or more years of experience (Preferred)

Benefits

Joining our team isn’t just a job — it’s an opportunity
One that takes your skills and pushes them to the next level
One that encourages you to challenge the status quo
One where you can shape the future of protection while supporting causes that mean the most to you
Meaningful impact

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

cloud infrastructureMLOpsCI/CDinfrastructure as codeautomationreliability engineeringdistributed systemsmodel versioningvalidationmonitoring

Soft Skills

leadershipcommunicationmentoringtechnical leadership