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 andattestations 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 secure. + 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

📌 Product Group Technology Lead - Ai 4 Data Management - C$149,500 - C$183,500 A Year (Toronto)
🏢 TD
📍 Toronto