DevOps, MLOps & Security Engineering Lead

Jobtailor; San Jose (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Jobtailor in San Jose seeks an experienced security‑focused engineering leader to own CI/CD governance, AI infra, and cross‑cloud security.

You will drive IaC, container orchestration, ML pipeline reliability, and incident response across AWS and GCP. This role blends development velocity with strong compliance and risk management in a fast‑moving environment.

Join a team that values security, scalability, and collaboration while delivering cutting‑edge AI capabilities.

Qualifications

• Hands-on DevSecOps leadership with ability to grow a high‑performing team.
• Extensive AWS and GCP experience, including IAM, compute, networking, and cost-aware resource management.
• Deep AI infrastructure experience: GPU/TPU provisioning, distributed training, model serving like SageMaker/Vertex AI.
• Strong cloud security knowledge across hybrid, multi-cloud environments.
• Proficient in Python, Bash, Go, or Java.
• Excellent communicator across developers, stakeholders, and executives.
• Familiarity with AI-assisted security is a strong advantage.
• Background in CS or Information Security.

Responsibilities

• Own the design and governance of CI/CD pipelines — automated testing, SAST/DAST, dependency checks, secrets detection.
• Lead infrastructure‑as‑code and container orchestration practices to scale and reduce manual effort.
• Apply engineering rigor to ML training pipelines, model serving infrastructure, and data supply chains.
• Design and manage the AI infrastructure layer across AWS and GCP.
• Ensure AI systems are built, deployed, and monitored to high reliability and security standards.
• Lead end-to-end security across cloud, infrastructure, and product — API protection and runtime security.
• Define and enforce security policies, standards, and best practices for delivery with compliance.
• Anticipate risks, drive preventative measures, and lead rapid incident response.
• Translate security and engineering requirements into actionable roadmaps.
• Define and track KPIs to show delivery effectiveness and prioritization.
• Act as a trusted security advisor to engineering squads and leadership.

Skills

DevSecOps leadership

AWS

GCP

CI/CD pipelines

Kubernetes

ML infra experience

Python

Communication

Security policy

Education

CS or InfoSec degree

Tools

EKS/GKE

SageMaker

Vertex AI

Terraform

IAM

Job description

Responsibilities

• 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.
• 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.
• 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.
• 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.

Requirements

• 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