DevOps, MLOps & Security Engineering Lead

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.

• 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