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page for more information. Work Location: Toronto, Ontario, Canada Hours: 37.5 Line of Business: Technology Solutions Pay Details: $149,500 - $183,500 CADTD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role. Job Description: Role Summary The PGTL for AI for Data Management is accountable for designing, building, and scaling AI methods and agent based services that automate and augment every aspect of Data Management - discovery, classification, cataloging, lineage, data products and quality, to governance workflows, evidence generation, and delivery lifecycle orchestration. This role owns the vision, implementation, and product leadership for AI and AI Agents that turn metadata, policies, and operational signals into actionable automation and continuous intelligence across cloud and on prem environments. Accountabilities: Product & Platform Ownership - Define and own the AI4DM product vision, roadmap, and value stream. Prioritize capabilities such as agent orchestration, LLM powered assistants, policy as code reasoning, and intelligent workflow automation. AI Methods & Agent Fabric - Establish reusable AI building blocks (LLMs, retrieval, guardrails, prompt/tooling frameworks, evaluators) and an agent fabric that can be composed to serve multiple Data Management capabilities. Lifecycle Coverage - Apply AI/Agents across the Data Management Delivery Lifecycle (plan build run optimize) and governance activities (standards, procedures, attestations, evidence packs), ensuring repeatable and audit ready outcomes. Engineering Leadership - Lead cross disciplinary squads (data platform, catalog/lineage, governance, DevOps/MLOps). Set engineering standards, SLAs/SLOs, and automation first practices for reliable, scalable AI services. Enterprise Integration - Integrate AI services with catalog, lineage, metadata lake, pipelines, ticketing/ITSM, workflow engines, and observability platforms to enable closed loop automation and continuous improvement . Adoption & Change Enablement - Drive adoption via reference architectures, onboarding kits, playbooks,



and reusable patterns; enable segments and domains to consume and extend AI capabilities safely and consistently. Key Responsibilities: Design & Build AI Capabilities + Architect LLM/RAG services, tool using agents, and rule learning components that perform discovery, classification, tagging, enrichment, lineage extraction, and data quality assistance. + Implement guardrails, prompt standards, evaluation harnesses, and safety policies for reliable outputs and traceable decisions. Agentized Delivery Lifecycle + Create delivery agents that assist with backlog curation, acceptance criteria, policy mapping, and evidence generation across Data Management initiatives. + Orchestrate run time agents for monitoring metadata freshness, control evidence assembly, exception triage, and remediation workflow handoffs. Governance Automation + Codify standards and procedures into policy as code libraries that agents can interpret and apply; generate dashboards and attestations that are audit ready by design. + Automate glossary alignment, role/ownership attribution, and lifecycle checkpoints to reduce manual governance effort. Data Quality & Intelligence + Use AI to recommend data quality rules, detect drift and anomalies, and prioritize high value exceptions; enable auto healing patterns where appropriate and protected. + Provide insight packs that summarize posture, coverage, and improvement opportunities across domains and platforms. MLOps & Reliability + Establish environments, pipelines, and telemetry for model lifecycle management (versioning, evaluation, rollback, observability). + Define service SLOs (latency, accuracy, freshness) and implement monitoring/alerting for agent reliability and output quality. Stakeholder Leadership + Partner with Data Governance, Privacy, Records & Information Management, Platform COEs, and domain teams to align policies, waivers, and technical design decisions. + Facilitate QBRs and program reviews; report outcomes, risks, and next best actions. Qualifications: 10+ years across data engineering, data governance/management; 5+ years leading platform/product teams at enterprise scale. 10+ years in AI/ML engineering and Data Science Demonstrated delivery of AI/agent based services for Data Management (e.g., catalog/metadata enrichment, lineage extraction, data quality assistance,



governance automation). Strong architecture skills with LLMs, retrieval systems, agent frameworks, event/workflow orchestration, and integration with catalog/lineage/metadata platforms. Proficiency in MLOps (model versioning, evaluation, telemetry, rollback), observability for AI services, and policy as code patterns. Expertise in taxonomy/glossary design, metadata quality, and lifecycle governance practices. Excellent stakeholder leadership and communication across governance, platform, and delivery teams. Experience with multi cloud and federated on prem environments; privacy preserving techniques; synthetic data generation for test harnesses; reinforcement learning from feedback to improve agent performance.The pay details posted reflect a temporary market premium specific to this role that is reassessed annually. Who We Are: TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we strive to make every interaction, product, and experience remarkably human and refreshingly simple for over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience. TD is deeply committed to being a leader in client experience, that is why we believe that all colleagues, no matter where they work, are client facing. Together, we are reimagining what banking can be for our clients, colleagues and communities. Our Total Rewards Package Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical, and mental well-being goals. Total Rewards at TD includes a base salary, variable compensation, and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs. Additional Information: We're delighted that you're considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we're committed to providing the support our colleagues need to thrive both at work and at home.Please be advised that this job opportunity is subject to provincial regulation for employment purposes. It is imperative to acknowledge that each province or territory within the jurisdiction of Canada may have its own set of regulations, requirements. Colleague Development If

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📌 Product Group Technology Lead - AI 4 Data ManagementDM) (Toronto)
🏢 TD Bank
📍 Toronto