Managing Director, Head of AI Operational Architecture & Integration

We are seeking a visionary and execution-oriented leader to serve as the Head of AI Integration & Operational Architecture. This role will own the design, integration, and operational architecture of the enterprise’s AI ecosystem—ensuring that AI capabilities are stable, scalable, and resilient. 

This leader will act as the system-level architect and integrator of AI across the enterprise, partnering closely with AI Engineering, Data, and AI Operations teams to enable AI at scale. The role sits at the intersection of platform architecture, product thinking, and operational reliability, transforming fragmented AI initiatives into a cohesive, enterprise-grade capability.

Key Responsibilities and Duties

1. Enterprise AI Ecosystem Integration

• Influence and maintain the end-to-end architecture of the AI ecosystem, including models, agents, orchestration layers, data pipelines, and platforms

• Enforce integration patterns and standards across internal systems and third-party AI tools

• Rationalize and streamline the AI stack to eliminate duplication and fragmentation

2. Operational Architecture for AI Systems

• Architect how AI systems operate in production environments at scale

• Design for fault tolerance, graceful degradation, and human-in-the-loop workflows

• Influence patterns for multi-model orchestration, routing, and fallback strategies

3. AI Platform Resilience & Reliability

• Define SLA/SLO frameworks for AI systems in partnership with AI Operations

• Architect solutions for high availability, Disaster Recovery, performance, and failure containment

• Partner with AI Ops on incident response models, runbooks, and recovery strategies

4. Influence AI Lifecycle & Deployment Architecture (AIDLC & Permit to Operate)

Define standards for model and AI capability lifecycle management, including:

• Versioning and release management

• A/B testing and canary deployments

• Rollback and fail-safe mechanisms

• Drive consistency across teams building AI solutions

5. Strategic Partnership with Technology Operations

• Establish clear separation of responsibilities: 
                   This role: architecture, integration, standards 
                  AI Ops: execution, monitoring, incident management

• Ensure seamless collaboration to deliver reliable, enterprise-grade AI systems

• Product Management for Observability, Monitoring & Telemetry including establishing enterprise-wide standards for AI observability

6. Enterprise Leadership & Influence

• Serve as the central authority on AI integration and operational architecture

• Influence senior stakeholders across Technology, Data, Risk, and Business units

• Drive alignment and adoption of enterprise AI standards

Educational Requirements

• University (Degree) Preferred

Work Experience

• 10+ Years Required

Career Level
11PL

10+ years in technology leadership roles, with deep experience in:
   •    Distributed systems architecture 
  •    Platform engineering or SRE 
  •    AI/ML systems at scale 
  •    Proven track record of building and operating complex, enterprise platforms 
  •    Integrating third-party and in-house AI/ML solutions

12–15+ years in technology leadership roles, with deep experience in:
    •    Distributed systems architecture 
    •    Platform engineering or SRE 
    •    AI/ML systems at scale 
    •    Proven track record of building and operating complex, enterprise platforms 
    •    Integrating third-party and in-house AI/ML solutions 
Technical Expertise
 Strong understanding of:

• AI/ML ecosystems (LLMs, agents, pipelines)
• Cloud platforms (AWS, Azure, GCP)
• Data engineering and real-time architectures

Familiarity with:

• MLOps / LLMOps frameworks
• Observability and monitoring tools
• API-driven and microservices architectures

Leadership & Mindset

• Systems thinker with the ability to operate at enterprise scale and complexity
• Strong product mindset—treating AI capabilities as platforms, not projects
• Ability to influence without authority across federated teams
• Balance of strategic vision and hands-on execution

What Success Looks Like

• A unified, well-integrated AI ecosystem across the enterprise
• Improved reliability, scalability, and performance for AI systems
• Reduced tool fragmentation and vendor sprawl
• Faster, safer deployment of AI capabilities
• Strong partnership with AI Ops, with clear a…