Director of AI Engineering Operations - Remote

DirectorDirectorFull TimeRemoteLeadTeam 1,001-5,000

Location

United States

Posted

5 days ago

Salary

Not specified

Seniority

Lead

No structured requirement data.

Job Description

What We’re About

At CentralSquare, we don’t just build software - we power public servants and uplift communities with Hero-Grade Technology. Every line of code, every feature we deliver helps heroes across North America protect, serve, and save lives. When you join us, you become part of a mission-driven team creating technology that makes communities safer and stronger.

Your Growth Matters. We believe heroes deserve opportunities to rise. That’s why we invest in your career with mentorship, learning programs, and clear paths for advancement. If you’re motivated, there’s no limit to how far you can go.

Your Commitment Deserves Reward. We offer competitive compensation and a benefits package designed to support your life inside and outside of work—tuition reimbursement, parental leave, paid volunteer hours, and unlimited PTO. Plus, our flexible work environment gives you the freedom to balance your heroic work with personal well-being, whether you’re in the office or remote.

Join us and help build the tools that power real-life heroes. Together, we make a difference.

 

The Opportunity 

The Director of AI Operations is a transformational leadership role within the CTO organization, chartered with fundamentally reimagining how product and engineering teams design, build, and deliver software. This leader will drive the enterprise-wide adoption of an AI-native Software Development Lifecycle — embedding AI capabilities directly into every stage of the development process, from requirements and architecture through testing, deployment, and iteration. The role carries clear accountability for measurable outcomes: accelerating development velocity, elevating code and product quality, and establishing the instrumentation needed to quantify productivity gains at both the team and individual level. Success in this role requires equal parts technical depth and organizational influence — the ability to assess where legacy processes are limiting throughput, redesign the way teams operate, and build the cultural and tooling foundations that make AI-native development the new standard across the portfolio. 

 

Key Responsibilities

AI Strategy & Program Management 

  • Own the AI program roadmap in partnership with the CTO, aligning initiatives to business priorities and technology strategy. 
  • Establish and lead the AI Operations Center of Excellence (CoE), setting standards for model development, deployment, monitoring, and governance. 
  • Drive portfolio-level visibility into all AI workstreams, ensuring executive stakeholders have clear line of sight into progress, risks, and ROI. 
  • Define and track KPIs for AI operational maturity, model performance, and business impact. 

AI Governance, Risk & Compliance 

  • Develop and enforce the company's AI governance framework, including responsible AI principles and model risk management standards. 
  • Collaborate with legal, security, and privacy teams to ensure AI deployments comply with applicable regulations (e.g., EU AI Act, CCPA, HIPAA where relevant). 
  • Champion ethical AI practices across the organization, facilitating training and awareness programs. 

Vendor & Ecosystem Management 

  • Manage relationships with AI platform vendors, cloud providers, and third-party model suppliers (e.g., OpenAI, Anthropic, Google, AWS, Azure AI). 
  • Lead evaluation, procurement, and contracting for AI tooling and foundation model APIs; negotiate SLAs and data processing agreements. 
  • Monitor the external AI landscape to surface emerging capabilities and competitive risks relevant to the company's strategy. 

Team Leadership & Culture 

  • Conduct a comprehensive assessment of existing SDLC processes across the portfolio and lead the strategic transformation of development teams toward an AI-native engineering model, establishing modern practices that embed AI capabilities throughout the full software delivery lifecycle. 
  • Foster a culture of experimentation, accountability, and continuous learning within the team. 
  • Develop talent pipelines and upskilling programs in partnership with HR and Learning & Development. 

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