# AVP, AI & Innovation at TD Bank

TD Bank is hiring an AVP, AI & Innovation in Toronto, Canada. Level rates it Builds AI ●●●●; you can [apply on TD Bank](https://jobsbylevel.com/go/fc6e97aa-2571-471d-b22d-c3052a460494?utm_medium=ai-agent).

AI Level 4, AI centrality 93 out of 100. Toronto, Ontario.

## Details

- Company: [TD Bank](https://jobsbylevel.com/companies/td-bank)
- AI level: AI Level 4 (score 93 out of 100)
- Location: Toronto, Ontario
- Posted: October 22, 2026
- Apply: https://jobsbylevel.com/go/fc6e97aa-2571-471d-b22d-c3052a460494?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=avp-ai-innovation-at-td-bank-e325da

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/avp-ai-innovation-at-td-bank-e325da

## Description

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

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