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Work Location:

Toronto, Ontario, Canada

Hours:

37.5

Line of Business:

Technology Solutions

Pay Details:

$126,800 - $164,100 CAD

This role is eligible for a discretionary variable compensation award that considers business and individual performance.

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 Global Transaction Banking (GTB) is a key growth business within TDS that provides the opportunity to make an impact with top‑tier organizations. We offer innovative solutions and treasury advisory services on large and complex liquidity, payments, and trade finance needs. The business constantly changes with macroeconomic conditions, unprecedented levels of innovation, interest rate environments, and foreign exchange movements. As a result, all GTB business lines continue to evolve to provide optimum trade finance, liquidity, and payment solutions to clients.

Global Transaction Banking technology team focuses on delivering top tier technology solutions to enable and grow the global GTB business.

Summary This role is a hands‑on Applied AI Engineer responsible for enabling AI adoption across GTB Channels initiatives. The engineer will design, build, and integrate AI‑driven solutions, tools, and workflows into real production systems serving engineering, quality, and delivery teams. The role emphasizes practical application of AI, not experimentation – building software that uses LLMs and intelligent automation to solve concrete problems such as development acceleration, quality validation, reporting, decision support,



and operational insight. The engineer will work closely with QE, Engineering, DevOps, and Architecture teams to embed AI capabilities into day‑to‑day delivery workflows.

Key Responsibilities

Applied AI Engineering

Design and implement AI‑powered features and services that support GTB Channels technology initiatives.

Build end‑to‑end AI solutions that integrate with existing enterprise systems, development pipelines, and delivery processes.

Apply LLM‑based techniques to automate and enhance activities such as:

Code and test generation, refactoring, and validation.

Intelligent analysis of defects, logs, metrics, and test results.

Automated documentation, release summaries, and engineering insights.

Decision support and signal extraction for delivery and quality readiness.

Develop production‑ready APIs and services that expose AI capabilities in a secure, governed, and reusable manner.

Use modern AI orchestration frameworks (e.g., LangChain‑style patterns) to build multi‑step, tool‑augmented AI workflows.

Integrate enterprise‑approved LLM platforms (e.g., Azure OpenAI or equivalent) into channel systems and internal tools.

Implement context‑aware AI solutions, incorporating structured inputs such as system metadata, configuration, requirements,



test artifacts, and operational telemetry.

Build reusable AI components and utilities that can be adopted by multiple teams and initiatives without duplication.

Primary Skills

Applied AI engineering and intelligent automation

Machine learning fundamentals (supervised/unsupervised learning, model evaluation)

Data science fundamentals (data preparation, feature engineering, statistical reasoning)

Large Language Models (LLMs) and prompt engineering

Agent‑based workflows and AI orchestration (e.g., LangChain‑style patterns)

Python and/or Java

REST APIs and backend service development

AI‑assisted tooling for development, testing, and reporting

CI/CD integration and automation

Observability, logging, and metrics for AI‑enabled systems

Qualifications

BS or MS degree in Computer Science, Engineering, or related field; or equivalent practical experience

5‑10+ years of hands‑on software engineering experience, building backend or platform systems

Proven applied AI experience, delivering LLM‑powered or intelligent automation solutions used in real environments

Solid understanding of AI system design, including context management, prompt strategies, and tool‑assisted reasoning

Experience integrating AI into existing enterprise workflows (engineering, QE, DevOps, or operational systems)

Solid grounding in software engineering fundamentals: design, testing, scalability, security, and maintainability

Comfortable working in regulated, enterprise environments with governance and risk considerations

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📌 Senior AI Engineer (Toronto)
🏢 TD
📍 Toronto