Director of AI Operations & Governance (r5464)

Shield AI Shield AI · Defense · Remote · Enterprise Operations

Director of AI Operations & Governance to operationalize and govern a workplace AI ecosystem, owning license/platform operations, AI governance, security, and lifecycle management. Requires hands-on technical capability to evaluate, tune, and troubleshoot models and AI systems for effective governance, reliability, performance, cost, and risk tradeoffs.

What you'd actually do

  1. Own license and platform operations, AI governance, security posture, and ongoing lifecycle management of AI tools that support Shield AI's business functions.
  2. Be the central owner of "post–dev-ops" for AI, ensuring systems are reliable, compliant, secure, and continuously improving in line with production usage and business needs, while building and leading the team responsible for AI sustainment.
  3. Credibly evaluate, tune, and troubleshoot models and AI systems at a technical level in order to govern them effectively, partner with engineering, and make sound tradeoffs between reliability, performance, cost, and risk.

Skills

Required

  • AI operations
  • AI governance
  • AI security
  • AI lifecycle management
  • Machine learning
  • Generative AI
  • Model evaluation
  • Model tuning
  • Model troubleshooting
  • System reliability
  • System compliance
  • System security
  • Performance optimization
  • Cost management
  • Risk management
  • Team leadership

Nice to have

  • License and platform operations
  • Post-dev-ops for AI
  • Continuous improvement
  • Production usage
  • Business needs alignment

What the JD emphasized

  • significant hands-on technical capability across the machine learning and generative AI lifecycle
  • evaluate, tune, and troubleshoot models and AI systems at a technical level

Other signals

  • operationalize AI
  • govern AI ecosystem
  • license and platform operations
  • AI governance
  • security posture
  • lifecycle management of AI tools
  • post-dev-ops for AI
  • systems are reliable, compliant, secure
  • continuous improvement
  • production usage
  • business needs
  • building and leading the team
  • AI sustainment
  • hands-on technical capability
  • machine learning and generative AI lifecycle
  • evaluate, tune, and troubleshoot models and AI systems
  • govern them effectively
  • partner with engineering
  • make sound tradeoffs between reliability, performance, cost, and risk