Technical Program Manager

What matters most

• AI Agent Performance and Quality Lifecycle Management

  The core of this role is owning the end-to-end health of AI agents used in Digital Asset compliance. You must establish robust telemetry, define KPIs for accuracy and velocity, and manage the continuous improvement loop from initial proof-of-concept to production deployment and post-launch monitoring.

• Translation of Compliance Policy into Technical Requirements

  You must act as the bridge between risk domain experts and engineering teams. This requires translating ambiguous, evolving policy changes and risk signals into concrete business requirements, evaluation criteria, and actionable backlog items that engineering can implement.

• Data-Driven Prioritization and Stakeholder Alignment

  Success depends on your ability to analyze performance trends, false positives, and operational bottlenecks to drive prioritization. You must communicate these insights effectively to both technical and non-technical stakeholders to ensure alignment on roadmap trade-offs and resource allocation.

Likely challenges to think through

• Balancing Compliance Rigor with AI Innovation

  You will face the difficulty of maintaining strict compliance and Responsible AI standards while pushing for automation speed and coverage. Prepare by demonstrating how you have managed trade-offs between rapid deployment and the necessity of rigorous testing, validation, and security guardrails.

• Managing Cross-Functional Dependencies in a Hybrid Environment

  The role requires coordinating across engineering, operations, compliance, and vendors. The challenge lies in maintaining momentum and alignment across diverse teams with potentially conflicting priorities. Prepare by highlighting your experience in managing complex dependencies and influencing stakeholders without direct authority.

• Translating Ambiguous Risk Signals into Actionable AI Improvements

  Compliance environments are often characterized by shifting regulations and complex, non-standardized inputs. The challenge is to maintain a stable, high-performing AI system despite these inputs. Prepare by discussing your methodology for decomposing ambiguous problems into structured experiments and measurable technical requirements.

Topics to prepare

• AI Agent Lifecycle Management

  Understand the end-to-end process of taking AI agents from proof-of-concept to production. Exercise: Map out a hypothetical lifecycle for an automated compliance agent, identifying key milestones for validation, go/no-go decision points, and operational handoff procedures to ensure alignment with the role's focus on continuous improvement.

• Compliance and Risk Translation

  Learn to bridge the gap between abstract regulatory policy and technical requirements. Exercise: Take a sample compliance policy and draft a set of measurable success criteria and KPIs that an engineering team could use to build or refine an automated detection agent.

Skills to revise

• Data-Driven Performance Analysis

  Use metrics to drive program improvements. Exercise: Create a mock dashboard layout using Power BI concepts that tracks agent accuracy, false positive/negative rates, and review velocity to identify bottlenecks in a compliance workflow.

• Backlog and Dependency Management

  Manage cross-functional technical programs. Exercise: Practice prioritizing a backlog of AI agent enhancements by weighing business impact against engineering effort and dependency risks, specifically for a scenario involving multiple stakeholders like operations and compliance.

• Machine Learning Evaluation

  Assess the quality of AI-enabled solutions. Exercise: Define a testing plan for an AI agent, including specific evaluation metrics for prompt quality and model performance, to ensure the solution meets production readiness standards.

• Map AI Lifecycle Experience to Compliance Automation

  Prepare a structured walkthrough of a past project where you managed an AI or automation solution from proof-of-concept to production. Highlight how you defined success criteria, monitored performance metrics, and handled the transition to operational support.

• Develop a Data-Driven Performance Framework

  Create a mock dashboard or report structure using Power BI concepts that tracks AI agent health. Include specific KPIs relevant to compliance, such as false positive/negative rates, review velocity, and coverage metrics, demonstrating how you use data to prioritize backlog items.

• Practice Translating Risk Policy into Technical Requirements

  Draft a sample requirements document that converts a hypothetical compliance policy change into actionable engineering tasks. Show how you would partner with risk domain experts to ensure the technical implementation aligns with business and Responsible AI standards.

• Prepare Cross-Functional Stakeholder Scenarios

  Identify examples of how you have managed dependencies and resolved conflicts between engineering, operations, and compliance teams. Be ready to discuss how you communicate technical tradeoffs and program risks to both technical and non-technical stakeholders.

• Formulate Targeted Questions for the Interviewers

  Prepare questions regarding the current maturity of the Digital Asset compliance automation, the primary challenges in scaling AI agent performance, and how the team balances rapid innovation with strict compliance and security requirements.