AVP, AI & Innovation

Work Location

Toronto, Ontario, Canada

Hours

37.5

Line Of Business

TD Securities

Pay Details

$155,000 - $215,000 CAD

TD 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 mandate

Lead the Applied AI and Innovation capability for TD Securities and build a high-performing team of applied AI specialists. Identify advances in AI that matter to the business and translate them into practical capabilities that can be tested, proven, and deployed in capital markets workflows.

This is a business-led senior technical leadership role requiring deep AI expertise, strong product judgment, and a pragmatic delivery mindset. The role connects emerging models, methods, and research with complex business problems and works across business, quantitative, data, engineering, and control teams to deliver measurable value.

Strategy and technical direction

Set the applied AI strategy, technical direction, and priorities across generative AI, agentic systems, decision intelligence, multimodal models, retrieval, reasoning, and human-agent operating models for TD Securities.
Assess research and industry developments in generative AI, agentic AI, machine learning, retrieval, reasoning, multimodal models, and related technologies, for practical relevance, then translate the most promising capabilities into experiments, product strategies, operating models, and value opportunities.
Determine where advanced AI provides a material advantage over conventional analytics, rules, or automation, avoiding unnecessary complexity.

Applied AI delivery and value creation

Identify and prioritize high-value opportunities; develop business cases, value hypotheses, adoption plans, measurable baselines, and value-realization frameworks.
Lead applied scientists in designing, building, testing, and improving AI solutions for complex, knowledge-intensive and analytical workflows.
Establish standards for experimentation, benchmarking, statistical rigor, error analysis, robustness, documentation, reproducibility, and production readiness.
Develop or adapt models and methods when existing solutions do not meet requirements for performance, latency, privacy, explainability, control, or cost.
Drive rapid prototyping while ensuring successful solutions can be engineered, governed, scaled, monitored, and supported in production.

Technical leadership

Provide senior technical oversight across model selection and adaptation, fine-tuning, retrieval-augmented generation, agent orchestration, knowledge and semantic methods, tool use, planning, memory, context management, hybrid retrieval, reranking, evaluation, and inference optimization.
Design robust agentic and retrieval systems, spanning tool use, planning, memory, context management, hybrid retrieval and reranking, while proactively addressing scaling, data quality, evaluation blind spots and inference cost.
Set rigorous experimentation and evaluation standards, including controlled ablations, multi-seed testing, statistical significance, red teaming and contamination controls to ensure solutions are reliable and production-ready.
Anticipate and mitigate scaling bottlenecks, evaluation blind spots, data quality issues, model risk, security concerns, and inference cost.
Challenge architecture and model choices; lead design reviews and resolve significant trade-offs with Engineering, Data, Architecture, Technology, and control partners.

AI products and capability development

Apply product-management disciplines to AI capabilities, including vision, roadmaps, lifecycle management, prioritization, adoption, and value tracking.
Work directly with quantitative practitioners, traders, bankers, risk specialists, data scientists, and engineers to frame problems and design practical AI solutions.
Establish reusable AI components, evaluation frameworks, implementation patterns, and technical standards that accelerate delivery across teams.
Apply AI to information extraction and synthesis, decision support, workflow automation, quantitative and analytical tasks, client intelligence, risk analysis, and other complex processes.
Partner with engineering and delivery teams to move validated prototypes into reliable production capabilities.

People and capability leadership

Build, lead, and develop a multidisciplinary applied AI team with a high bar for technical quality, scientific rigor, engineering discipline, and measurable impact.
Lead through technical credibility by authoring design documents, running architecture and technical reviews, and setting expectations for code, experiments, and system quality.

Governance, risk, and responsible adoption

Partner with Risk, Compliance, Legal, Information Security, Technology, and enterprise AI governance teams to ensure responsible, controlled, auditable, and explainable adoption.
Define guardrails, escalation paths, adoption controls, measurement routines, and governance artifacts for AI products and agentic workflows, while preserving clear human accountability for material decisions.

Decision authorities

Prioritize the AI product and transformation portfolio with front-office sponsors and enabling partners.
Determine which emerging capabilities warrant experimentation and which prototypes have sufficient evidence to progress toward production.
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