Banking Technology Architecture Lead

Citi•Mississauga, ON

2d•Hybrid

About The Position

The Banking Technology Architecture Lead – NAM is a senior strategic position responsible for bridging distributed engineering teams and enterprise architecture strategy across the North America region. Operating within the Banking Technology organization, this role drives measurable delivery improvement through platform adoption, impediment resolution, and AI-first methodologies. The overall objective is to accelerate engineering delivery velocity by embedding architectural best practices and intelligent, data-driven decision-making at the team level — with AI serving as the primary force multiplier for regional impact. We are seeking a strategic and hands-on Architecture Lead – NAM to serve as the connective tissue between enterprise architecture vision and regional engineering execution. This role is designed not as a traditional architecture enforcement function, but as a role actively listening for systemic delivery pain points and translating them into actionable, AI-augmented solutions that remove obstacles and accelerate engineering teams. The ideal candidate will be deeply AI-first in their thinking and practice, leveraging AI-powered tools, analytics, and automation as a primary force multiplier to drive adoption of standardized platforms, reduce lead times, and deliver data-driven architectural recommendations at scale. AI fluency is not optional — it is a core success factor for this role. Candidates who do not actively use and champion AI tooling in their day-to-day practice will not be competitive for this position. This position covers NAM engineering teams with late EMEA overlap, operating as an embedded liaison with dotted-line relationships to engineering teams and solid-line accountability to architecture leadership.

Requirements

8+ years in software engineering, platform engineering, or enterprise architecture roles, with 3+ years in a senior or lead capacity.
Demonstrated experience driving platform or tooling adoption across distributed engineering teams.
Proven AI-first mindset: hands-on, day-to-day experience using AI tools, analytics platforms, or LLM-based solutions to improve engineering workflows, delivery metrics, or decision-making — this is a non-negotiable requirement.
Strong analytical skills with the ability to translate delivery telemetry and impediment data into actionable architectural recommendations.
Familiarity with DORA metrics and software delivery lifecycle performance frameworks (lead time, cycle time, deployment frequency, change failure rate).
Experience working across cross-functional teams including DevOps, platform engineering, and developer experience functions.
Excellent communication and stakeholder management skills, with a demonstrated ability to influence without direct authority — essential given the embedded liaison operating model.
Bachelor's degree in Computer Science, Engineering, or equivalent experience

Nice To Haves

Master's degree preferred
Experience with AI-powered developer tooling, delivery analytics platforms, or intelligent automation frameworks (e.g., GitHub Copilot, AI-assisted observability tools, LLM-based workflow automation).
Familiarity with enterprise architecture frameworks (e.g., TOGAF, C4 Model) and their practical application in large-scale organizations.
Prior exposure to Golden Path or Internal Developer Platform (IDP) programs.
Experience with observability, platform telemetry, and engineering metrics dashboards.
Familiarity with engineering maturity models or capability assessments used to benchmark team adoption and delivery performance.
Exposure to enterprise AI governance standards, including model explainability, fairness, and compliance frameworks.
Advanced degree in Computer Science, Systems Engineering, or a related technical field.

Responsibilities

Drive measurable adoption of Citi Manifesto principles, Golden Paths, and Foundational platforms across NAM engineering teams.
Translate enterprise architecture vision into practical, actionable guidance that accelerates delivery velocity.
Achieve quantifiable adoption metrics through hands-on enablement and demonstration of platform value.
Leverage AI-powered analytics to identify adoption patterns, surface friction points, and personalize enablement strategies for different team contexts and maturity levels.
Build trusted relationships with engineering team leads by acting as a visible, accessible partner — not an oversight function — to encourage voluntary adoption of architectural standards.
Systematically collect, document, and analyze real engineering bottlenecks and recurring delivery obstacles using AI-driven analysis across incident reports, delivery metrics, and workflow telemetry.
Deploy AI-powered delivery analytics tools to identify lead time bottlenecks across the full software delivery lifecycle — spanning planning, development, testing, and deployment.
Champion an AI-first approach to continuous improvement, ensuring all recommendations are grounded in data and augmented by intelligent tooling rather than anecdotal observation.
Maintain reliable, cadenced communication with the Head of Architecture (weekly/bi-weekly), providing structured updates on impediments, adoption progress, and improvement outcomes.
Manage enterprise architecture processes to ensure they enhance — rather than hinder — delivery velocity.
Provide data-driven evidence of lead time improvements attributable to architecture initiatives, using AI-generated insights and delivery dashboards where applicable.
Ensure AI tools and analytics platforms adopted within the architecture function comply with enterprise AI governance standards, including explainability, fairness, and data privacy requirements — collaborating with risk, legal, and compliance teams as needed.