# Director of AI Operations & Governance

**Company:** [Shield AI](https://hotfix.jobs/companies/shield-ai)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $280k – $420k/yr
**Experience:** 15+ years
**Skills:** Machine Learning, Generative AI, LLMs, Prompt Engineering, RAG, agentic frameworks, model fine-tuning, Model Evaluation, drift detection, ai governance, ai security, Observability, APIs, Integrations, prompt libraries
**Posted:** 2026-07-20

> Director owning post-dev-ops for workplace AI at Shield AI: leads sustainment, governance, security, and lifecycle management of ML/LLM tools while providing hands-on technical oversight of models, tuning, and operations. Requires 15+ years experience including direct ML development and people leadership.

## Job Description

## Technical AI/ML Ownership
- Evaluate, benchmark, fine-tune, and adjust configurations of ML and generative AI models (including LLMs) in production.
- Apply applied AI/ML research and emerging techniques to inform build-vs-buy decisions, model selection, and architecture choices.
- Partner directly with data science and ML engineering teams on model performance issues, drift detection, and retraining or reconfiguration needs.
- Maintain technical fluency in prompt engineering, retrieval-augmented generation, agentic/orchestration frameworks, and distinctions between generative AI and traditional ML systems.

## AI Sustainment & Governance
- Own AI sustainment and governance for workplace AI tools, acting as the central "run" function.
- Manage all AI-related licenses and entitlements, monitor usage, optimize allocations, and partner with Finance for cost visibility.
- Monitor production usage patterns and performance to recommend roadmap items, enhancements, and deprecations.
- Own and triage support tickets for workplace AI tools, driving resolution across vendors, engineering, and security.
- Lead continuous evaluations of AI tools and models, including monitoring drift and benchmarking performance.
- Maintain updates and security outlook for AI platforms, coordinating patches, upgrades, vulnerability remediation, and compliance.
- Orchestrate model swaps and configuration changes in production, including rollout planning, risk assessment, and monitoring.
- Design, maintain, and govern shared prompt libraries, including standards for quality, reuse, versioning, and training.
- Own management of secrets (API keys, credentials, tokens) for secure storage, rotation, and access control.
- Define and maintain connectors and extensions for reliable, secure data access in AI workflows.
- Establish and operate auditability frameworks for AI tools, including logging and traceability.
- Lead AI governance practices (policies, guardrails, usage standards, approval workflows) in partnership with Security, Legal, and HR.
- Partner with business solution and build teams to ensure deliverables meet sustainment, observability, and governance requirements.
- Define operational playbooks, SLAs, and incident response procedures for AI systems.

## Leadership & Strategy
- Build, lead, and develop a team of AI operations professionals, contractors, and platform specialists.
- Set the strategic direction for AI operations and governance, translating priorities into a multi-quarter roadmap and budget.
- Provide regular status and risk updates to the VP of Workplace AI and senior leadership on adoption, reliability, governance, and cost trends.

## Required Qualifications
- 15+ years in platform operations, ML/AI operations, DevOps, or SaaS sustainment roles, with significant leadership/people management experience running production systems in high-stakes environments.
- Direct, hands-on experience developing, training, fine-tuning, or evaluating machine learning models or generative AI systems.
- Working knowledge of AI/ML research practices and ability to apply current research to production decisions.
- Software engineering or data science background to engage deeply with technical teams.
- Direct experience with AI platforms or orchestration tools (e.g., LLM providers, RPA/workflow tools like n8n, enterprise SaaS integrations) and their operational management.
- Demonstrated understanding of distinct technical and operational challenges of generative AI versus traditional ML.
- Strong background in governance, compliance, or security for data-driven or AI systems.
- Experience managing licenses and cost optimization for SaaS or AI tools at scale, including budgets.
- Hands-on experience with monitoring and observability stacks to shape roadmaps.
- Strong technical fluency across APIs, connectors, and integrations.
- Proven track record building and leading high-performing teams of staff and contractors.
- Excellent executive communication skills.
- Experience operating in a hybrid environment of contractors and core team members.

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