Principal Delivery Lead, AI Evaluation & Governance Platform

Job Description

What's the opportunity?

The AI Group at RBC is building the enterprise platform capabilities that help teams evaluate, govern, and scale AI systems safely across the bank.

We are looking for a Principal Delivery Lead to support the AI Evaluation & Governance Platform, including our model and agent evaluation platform. This platform helps teams evaluate AI models, agents, applications, prompts, tools, and runtime behavior through repeatable evaluation workflows, evidence capture, certification support, and ongoing monitoring.

This is a technical delivery leadership role. You will work at the intersection of engineering, product, AI architecture, governance, risk, and platform teams to turn roadmap priorities into executable delivery plans. You will create the operating discipline needed to deliver complex AI platform capabilities with clear milestones, Jira-backed visibility, dependency management, risk tracking, and leadership-ready reporting. Neither a traditional project coordination role nor a product management role, the Product Manager owns the product direction, customer narrative, requirements, and roadmap story. The Delivery Lead owns the execution system: how the work is organized, tracked, sequenced, reported, and delivered with confidence.

Partner closely with engineering leaders, product managers, architects, AI governance stakeholders, and platform teams to help the organization move faster without losing delivery clarity or control.

What will you do?

Lead delivery execution for the AI Evaluation & Governance Platform

• Own delivery planning and execution tracking across key platform workstreams.
• Translate roadmap priorities into delivery plans, milestones, dependencies, and measurable commitments.
• Partner with engineering leaders to understand capacity, sequencing, delivery risks, and path-to-green.
• Maintain clear visibility into delivery health across build-time evaluations, runtime evaluations, evidence workflows, certification support, and platform integrations.
• Ensure teams can make credible delivery commitments based on real execution signals.

Create strong operating discipline without heavy process

• Establish a lightweight but effective delivery rhythm across engineering, product, and stakeholder teams.
• Support team-level execution while preserving engineering autonomy.
• Help teams use Agile, Scrum, Kanban, or hybrid delivery practices in a practical way that fits the work.
• Avoid unnecessary ceremony while ensuring commitments, risks, blockers, and decisions are visible.
• Drive clarity across who owns what, what is on track, what is at risk, and what needs leadership attention.

Own Jira discipline and delivery visibility

• Ensure Jira is the system of record for execution tracking.
• Partner with engineering teams to maintain clean epics, stories, owners, statuses, dependencies, milestones, and delivery dates.
• Make Jira useful for leadership reporting, not just team-level task tracking.

Manage dependencies, risks, and blockers

• Identify, track, and proactively manage cross-team dependencies.
• Surface delivery risks early and drive path-to-green discussions.
• Coordinate across engineering, product, architecture, governance, risk, SRE, security, and partner platform teams.

Support leadership reporting and control book inputs

• Provide delivery evidence for monthly leadership reporting and control book updates.
• Track milestone confidence, execution risks, blockers, dependencies, and delivery progress.
• Partner with Product Managers on the product narrative, roadmap context, and stakeholder impact.
• Help Senior Directors and the VP of AI Architecture, Tools and Innovation maintain a clear view of delivery health.

Partner with Product Managers and engineering leaders

• Work with Product Managers to connect roadmap priorities with delivery reality.
• Validate timeline confidence, capacity constraints, and dependency impacts.
• Support scope trade-off conversations when timelines, resources, or priorities conflict.
• Help define MVP delivery plans in partnership with product and engineering.
• Maintain a clear separation between product ownership and delivery ownership.

What do you need to succeed?

Must-have

• Experience leading complex technical delivery across software engineering, platform engineering, AI/ML, data, cloud, or enterprise technology teams.
• Strong technical program management, delivery management, or project leadership experience in a technology organization.
• Ability to operate across engineering, product, architecture, risk, governance, and senior leadership stakeholders.
• Experience using Jira or similar tools to manage epics, milestones, dependencies, delivery status, and reporting.
• Strong understanding of software delivery lifecycles, Agile delivery practices, dependency management, release planning, and risk tracking.
• Ability to translate ambiguous goals into structured execution plans.
• Strong written and verbal communication skills, including executive-ready status reporting.
• Ability to identify risks early, drive clarity, and create practical path-to-green plans.
• Strong judgment on when to add process and when to keep teams moving.
• Comfort working in a fast-moving AI platform environment where scope, dependencies, and priorities evolve.

Technical depth expected

• Familiarity with AI, ML, GenAI, LLMs, agentic systems, or AI platform delivery.
• Ability to understand technical discussions well enough to identify delivery risks, dependencies, and sequencing issues.
• Comfort working with engineers, architects, applied scientists, product managers, and governance stakeholders.
• Ability to connect platform delivery work to broader outcomes such as evaluation, evidence, certification, governance, monitoring, reuse, and safe AI adoption.

Nice-to-have

• Experience with AI governance, model risk management, responsible AI, AI evaluation, model validation, or regulated AI delivery.
• Experience with ML platforms, evaluation frameworks, observability platforms, CI/CD, data pipelines, or runtime monitoring.
• Experience supporting platform teams that serve many internal customers.
• Experience working in financial services or another regulated environment.
• Experience with control book-style reporting, portfolio reporting, or VP/EVP-level operating reviews.
• Familiarity with tools or concepts such as MLflow, Langfuse, model evaluation, agent evaluation, trace analysis, evidence stores, release gates, or policy/control workflows.

What success looks like

In the first 90 days, you will:

• Establish a clear delivery view for the AI Evaluation & Governance Platform.
• Bring structure to Jira across the core workstreams.
• Clarify milestones, dependencies, risks, and delivery confidence.
• Partner with product and engineering to connect roadmap priorities to executable plans.
• Create a reliable delivery input into leadership reporting and control book updates.

Over time, you will:

• Help the team scale delivery across model evaluation, agent evaluation, runtime validation, evidence capture, and governance workflows.
• Improve delivery predictability without introducing heavy process.
• Help leadership understand what is on track, what is at risk, and what decisions are needed.
• Enable product and engineering teams to move faster with better visibility and fewer surprises.
• Build a delivery operating model that can be reused across AI Architecture, Tools and Innovation.

What's in it for you?

• Work on one of RBC's most important AI platform capabilities: helping teams evaluate and govern models, agents, and AI applications at enterprise scale.
• Partner with senior leaders across AI architecture, engineering, product, governance, and risk.
• Help shape the operating model for how AI evaluation and governance platforms are delivered across the bank.
• Work in a highly technical environment with engineers, architects, scientists, and product experts.
• Build delivery discipline for a platform that sits at the center of safe, scalable AI adoption

#LI-POST

#TECHPJ

Job Skills

AI Architecture, AI Concepts, AI Governance, AI Systems, Artificial Intelligence (AI), Competitive Markets, Computing, Conceptual Thinking, Data Science, Product Development Lifecycle, Product Development Methodology, Product Management, Product Manufacturing, Product Requirements, Product Testing, Prototype Manufacturing, Secure AI, Value Realization

Additional Job Details

Address:

RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTO

City:

Toronto

Country:

Canada

Work hours/week:

37.5

Employment Type:

Full time

Platform:

TECHNOLOGY AND OPERATIONS

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-06-05

Application Deadline:

2026-06-19

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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