DevOps, MLOps & Security Engineering Lead - San Jose, CA

Team: US Team

Location: California

Commitment: Full-Time

Workplace Type: hybrid

Salary:

What You'll Do

DevOps & CI/CD
Own the design and governance of CI/CD pipelines — including automated testing, SAST/DAST scanning, dependency checks, and secrets detection. Lead infrastructure-as-code and container orchestration practices, and drive automation initiatives that reduce manual effort and improve consistency at scale.

MLOps & AI Infrastructure
Apply engineering rigor to ML training pipelines, model serving infrastructure, and data supply chains. Design and manage the AI infrastructure layer — including GPU/compute resource provisioning, model registry operations, experiment tracking, and inference scaling — across AWS and GCP. Ensure AI systems are built, deployed, and monitored to the same reliability and security standards as core product services.

Security
Lead end-to-end security across cloud, infrastructure, and product — spanning cloud posture management, API protection, runtime security, network segmentation, and secrets management across AWS and GCP environments. Define and enforce security policies, standards, and best practices that balance delivery speed with a strong compliance posture. Anticipate operational risks, drive preventative measures, and lead rapid incident response across environments.

Strategy & Leadership
Translate security and engineering requirements into actionable roadmaps. Define and track KPIs that demonstrate delivery effectiveness and inform prioritization. Act as a trusted security advisor to engineering squads and leadership alike.

What We Are Looking For

A proven engineering leader with hands-on depth in DevSecOps, capable of growing and inspiring a high-performing team
Strong hands-on knowledge of AWS and GCP — including compute, networking, IAM, managed Kubernetes (EKS/GKE), cloud-native security tooling, and cost-efficient resource management across both platforms
Deep experience managing AI infrastructure — GPU/TPU provisioning, distributed training environments, model serving platforms (e.g. SageMaker, Vertex AI), and inference optimization at scale
Strong knowledge of cloud security, infrastructure security, and modern CI/CD platforms across hybrid, multi-cloud environments
Proficient in scripting and development — Python, Bash, Go, or Java
A confident communicator who can translate priorities clearly across developers, stakeholders, and executives
Familiarity with AI-assisted security — threat detection, anomaly detection, intelligent vulnerability triage — is a strong advantage
Background in Computer Science, Information Security, or equivalent practical experience