Engineering Manager - Cloud & AI Runtime

CrowdStrike CrowdStrike · Enterprise · Bangalore, India

Engineering Manager for CrowdStrike's Cloud Runtime Protection team, focusing on securing cloud-native and AI workloads with eBPF and high-performance sensor features. The role involves leading a team to build scalable Linux runtime security solutions for AI/ML infrastructure across millions of systems.

What you'd actually do

  1. Lead a world class team build comprehensive highly scalable and highly performant linux runtime security solutions for Cloud and AI workloads deployed at scale
  2. Engage and influence cutting edge security designs of Major Cloud & AI service Providers through Crowdstrike's engineering partnerships
  3. Ensure systems and components reliability and performance through monitoring, testing, and debugging. Lead debugging product issues found through test or customer cases to identify root cause and use the input to improve tests.
  4. Work closely with your team to support and continue a culture of high product quality and excellence. Collaborate with cross-functional teams to integrate sensor and cloud solutions.
  5. Work collaboratively with product and release management to control risk, improve quality, and streamline our release cycles

Skills

Required

  • 10+ years of experience working and managing product features on Linux or Unix in C/C++
  • Experience in leading, mentoring and growing a team of highly skilled engineers
  • Experience developing production eBPF code for security or networking
  • Experience in programming cloud native workload using public cloud platforms, Private Cloud Platforms and container technologies (like Kubernetes, Docker…)
  • Experience leading a team to ship major features and releases
  • Ability to communicate, collaborate, and work effectively in a distributed team

Nice to have

  • secure coding
  • testing paradigms
  • debugging
  • performance measurement
  • code reviews
  • CI/CD
  • OS internals

What the JD emphasized

  • AI workloads
  • Machine Learning infrastructure
  • eBPF
  • high concurrency requirements
  • high reliability requirements
  • low-level operating characteristics

Other signals

  • AI-native platform
  • AI workloads
  • Machine Learning infrastructure