Devops Tech Lead

Unity Unity · Enterprise · Tel Aviv, Israel · Engineering

Seeking a DevOps Tech Lead to provide technical leadership for multiple DevOps teams, driving best practices and operational excellence. The role involves architecting scalable cloud infrastructure for AI/ML workloads, optimizing data infrastructure, enhancing developer productivity, and implementing AI observability. Requires 7+ years in DevOps/SRE/platform engineering with 2+ years of leadership experience, deep Kubernetes expertise, and proficiency in IaC and cloud providers.

What you'd actually do

  1. Technical Leadership: Shape the future of Aura's infrastructure and influence architectural decisions across the organization.
  2. Wide Range of Technologies: Work across the full DevOps spectrum – from IaC and Kubernetes to data platform and developer productivity tools.
  3. Personal Development: Clear path for career advancement as you demonstrate success and impact — at the intersection of DevOps and AI infrastructure, one of the fastest-growing engineering domains.
  4. Innovation Playground: Experiment with emerging technologies and implement solutions at scale — including GPU scheduling, vector databases, and LLM serving frameworks.
  5. Strategic Impact: Direct line of sight between your work and Aura's business outcomes

Skills

Required

  • 7+ years in DevOps, SRE, or platform engineering
  • 2+ years leading teams or tech-leading across multiple squads
  • Deep expertise with Kubernetes — architecture, networking, security, and cluster operations at scale
  • Hands-on experience with Pulumi (or equivalent IaC)
  • at least one major cloud provider (AWS, GCP, or Azure)
  • Strong CI/CD expertise — designing and maintaining pipelines at scale (e.g., GitHub Actions, Jenkins, ArgoCD)
  • Solid understanding of networking, security, and observability in cloud-native environments
  • Experience with monitoring and observability stacks (e.g. Prometheus, Grafana)
  • Strong background in programming skills (Python, Typescript, etc.)
  • Proven ability to define technical strategy, drive cross-team alignment, and mentor engineers

Nice to have

  • Experience building infrastructure for AI/ML workloads (GPU scheduling, model serving, training pipelines)
  • Familiarity with streaming and data platforms (Kafka, Spark, Airflow, Ray)
  • Contributions to open-source DevOps or infrastructure projects

What the JD emphasized

  • AI and machine learning platform layer
  • AI/ML training
  • real-time model serving
  • data scientists and ML engineers deploy and monitor models
  • Optimize Data & AI Infrastructure
  • Implement AI Observability
  • AI infrastructure
  • GPU scheduling
  • vector databases
  • LLM serving frameworks