Senior AI Platform Engineer

About this role

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world's largest in-line cloud security platform. We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world's hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role As a Senior AI Platform Engineer, you will design, build, and maintain scalable, secure, and highly available AWS infrastructure optimized for complex AI/ML workloads. Utilizing Terraform and modern IaC principles, you will establish robust cloud architectures, automate release pipelines, and architect comprehensive observability and alerting solutions. Operating with a high degree of autonomy, you will champion platform reliability, security, and governance while partnering with AI/ML teams to deploy production-grade systems and mentor engineers across the organization. What You'll Do (Role Expectations) Design, build, and maintain scalable, secure, and highly available AWS infrastructure (EKS, Lambda, ECS, VPC, IAM) for AI/ML workloads using Terraform best practices. Own and continuously evolve GitLab CI/CD pipelines for AI platform services to enable fast, reliable, and repeatable automated releases. Architect a centralized observability stack using Prometheus and Grafana with golden-signal dashboards and design intelligent alerting strategies to lead efficient incident response. Define and track DORA metrics to drive delivery and reliability improvements while building self-healing, auto-scaling, and cost-optimized infrastructure for AI/ML services on Kubernetes. Implement infrastructure security best practices, establish platform governance standards, partner on production-grade AI/RAG deployments, and mentor junior/mid-level engineers. Who You Are (Success Profile) You are expert at designing and running production-grade environments, specializing in AWS cloud-native architectures tailored specifically for high-demand AI/ML workloads. You are driven to automate everything from infrastructure provisioning to multi-environment code deployment, ensuring systems are repeatable, secure, and easily maintained. You rely on concrete observability tools and DORA metrics to diagnose bottlenecks, resolve production incidents, and continuously improve platform reliability and delivery speeds. You enjoy working closely with cross-functional AI/ML and data teams to bridge the gap between development and operations, while actively mentoring peers. You work exceptionally well independently, taking complete ownership of complex infrastructure initiatives and communicating technical concepts with Staff-level clarity. What We're Looking For (Minimum Qualifications) Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain. 4+ years of experience as a Platform, SRE, or DevOps Engineer, including 1+ years supporting AI/ML or data platform infrastructure, with deep hands-on AWS skills (EKS, Lambda, ECS, VPC, IAM, S3). Strong hands-on experience using Terraform (modules, workspaces, remote state) for Infrastructure as Code and designing/maintaining GitLab CI/CD pipelines with runners and automated deployments. Proven expertise in centralized monitoring using Prometheus and Grafana (custom exporters, dashboards, alerting) alongside tuning alerting systems (Alertmanager, PagerDuty, OpsGenie). Solid, hands-on understanding of DevOps/DORA metrics (deployment frequency, lead time for changes, change failure rate, MTTR) to measure and drive engineering improvements. Experience operating containerized apps on Kubernetes (EKS, Helm, Karpenter/HPA), strong scripting skills in Python (and/or Go/Bash), and excellent communication skills. What Will Make You Stand Out (Preferred Qualifications) Demonstrate a proven ability to leverage AI technologies and workflows to drive measurable operational efficiency. Experience with LiteLLM (multi-region deployments), distributed compute frameworks like Ray.io/Anyscale, workflow engines like Temporal, and agentic frameworks (LangGraph, LangChain, or LlamaIndex). Working knowledge of vector databases (Qdrant, Pinecone, Weaviate), memory/context layers like ZEP, MLOps practices (model registry, MLflow/Kubeflow), and LLM observability tools (Arize Phoenix). Exposure to GitOps workflows (ArgoCD/Flux), log aggregation and distributed tracing (Loki, ELK, Jaeger/Tempo), cloud cost optimization (FinOps), and security compliance frameworks (SOC 2, ISO 27001). #LI-Hybrid #LI-NT1 At Zscaler, we are committed to building a team that reflects the communities we serve and the customers we work with. We foster an inclusive environment that values a

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