AVP/VP, Technical Project Manager (AI), Technology Group

What matters most

• Dual-Model Delivery Orchestration

  You must simultaneously manage two distinct operational rhythms: long-lived AI Foundation platform development and time-boxed Forward Deploy engagements. Success requires maintaining synchronization between these models while ensuring both meet their respective milestones and strategic objectives.

• Cross-Functional Dependency Management

  The role requires proactive coordination with enterprise-level groups including Cybersecurity, Infrastructure, and Data Technology. You must track these external dependencies, escalate blockers early, and ensure that AI engineering progress is not stalled by enterprise-wide governance or resource constraints.

• Operational Scaling and Process Evolution

  As the team grows from 14 to 25+ members, you are responsible for evolving delivery processes from founding-squad informality to scalable, structured workflows. This includes managing the logistics of hiring, onboarding, and integrating a mixed workforce of permanent staff and augmented vendor resources.

• Technical Credibility and Stakeholder Reporting

  You must act as a delivery partner who understands distributed systems and AI workloads well enough to challenge estimates and identify architectural risks. You are expected to translate this technical reality into clear, honest status reporting for senior leadership, focusing on project health and risk posture.

Likely challenges to think through

• Balancing Rigor with Agility in AI

  AI development involves non-deterministic scope and rapid framework evolution, which can conflict with traditional project management structures. Prepare by demonstrating how you have applied 'just-enough' process to keep teams accountable without stifling the experimentation necessary for AI-native engineering.

• Managing a Mixed Permanent-Vendor Workforce

  Integrating augmented vendor resources into a cohesive, high-performing culture is a significant challenge. Prepare to discuss how you maintain consistent performance standards, ensure knowledge transfer, and foster team unity when team members have different contractual relationships and tenure.

• Navigating Highly Regulated Environments

  Operating within a sovereign wealth fund requires strict adherence to governance and compliance. The challenge is to integrate these requirements into the delivery lifecycle without creating bottlenecks. Prepare to explain how you have successfully navigated review panels and compliance gates in previous roles.

Topics to prepare

• Managing Dual-Track Delivery Models

  The role requires balancing long-lived platform engineering squads with time-boxed Forward Deploy squads. Exercise: Draft a hypothetical roadmap that reconciles the continuous improvement cycle of a platform team with the fixed-deadline requirements of a client-facing deployment squad. This is relevant because the role demands keeping these distinct rhythms synchronized.

• AI/ML Development Lifecycle Dynamics

  AI projects involve non-deterministic scope and rapid framework evolution. Exercise: Create a framework for managing 'research vs. engineering' tension, specifically how to define 'Definition of Done' for an AI agent project versus a standard infrastructure build. This is relevant as the role requires deep technical understanding to challenge estimates and manage risks in AI workloads.

• Scaling Engineering Operations

  The team is growing from 14 to 25+ members. Exercise: Outline a plan to transition from a single-squad process to a multi-squad structure, identifying which ceremonies (e.g., retrospectives, planning) need to be centralized versus decentralized. This is relevant because the role involves building scalable processes that do not introduce unnecessary bureaucracy.

Skills to revise

• Advanced JIRA Backlog Management

  The role requires maintaining a 'source of truth' rather than a graveyard of tickets. Exercise: Design a JIRA dashboard structure that tracks cross-team dependencies with cybersecurity and infrastructure groups, including custom filters for 'at-risk' items. This is relevant because the role demands high-level visibility into project health for senior leadership.

• Vendor and Augmented Workforce Coordination

  Managing a mixed permanent-and-vendor workforce is a core requirement. Exercise: Develop an onboarding checklist and a performance tracking template specifically for augmented resources to ensure they are productive from day one. This is relevant as the role involves managing a significant portion of the workforce through external contracts.

• Cross-Functional Dependency Management

  Coordinating with enterprise functions like IAM and Cybersecurity is critical. Exercise: Create a dependency mapping matrix for a hypothetical AI platform feature that requires integration with enterprise security and data teams. This is relevant because the role requires navigating large, regulated organizations to unblock engineering teams.

• Prepare a case study on managing dual-delivery models

  Develop a structured walkthrough of a past project where you managed both long-lived platform engineering squads and time-boxed, mission-specific squads. Highlight how you balanced different cadences, prioritized backlogs, and maintained visibility for leadership across both workstreams.

• Document your approach to vendor and workforce integration

  Prepare specific examples of how you have successfully onboarded and managed augmented or vendor resources within a permanent engineering team. Focus on how you maintained team culture, enforced quality standards, and ensured productivity in a mixed-workforce environment.

• Refine your technical communication strategy

  Prepare a summary of how you translate complex technical risks—such as those involving distributed systems, AI/ML workloads, or infrastructure dependencies—into clear, actionable insights for senior stakeholders. Be ready to explain how you maintain JIRA hygiene to ensure it serves as a reliable source of truth.

• Formulate questions regarding GIC's AI engineering roadmap

  Prepare thoughtful questions about the current maturity of the AI Foundation platform, the specific challenges of scaling from a founding squad to a multi-squad organization, and how the team currently balances research-driven experimentation with production-grade delivery requirements.

• Draft a plan for 'just-enough' process implementation

  Outline your philosophy on establishing delivery processes that scale. Be prepared to discuss how you would introduce or adapt sprint cadences, retrospectives, and 'definition of done' criteria to support a team growing from 14 to 25+ members without introducing unnecessary bureaucracy.