Venture outside the ordinary - TMX Careers

The TMX group of companies includes leading global exchanges such as the Toronto Stock Exchange, Montreal Exchange, and numerous innovative organizations enhancing capital markets. United as a global team, we’re connecting cross-functionally, traversing industries and geographies, moving opportunity into action, advancing global economic growth, and propelling progress. Through a rich exchange of ideas, meaningful collaboration, and a nimble operating model, we're powering some of the nation's most critical systems, fueling capital formation and innovation, bringing increased opportunity to business visionaries, product ingenuity to consumers, and career exploration to our team.

Ready to be part of the action?

Key Responsibilities

- Define and execute an integrated AI and data roadmap in partnership with business and technology leaders.
- Build, coach, and retain a high-performing, diverse team spanning data engineering, and AI engineering; manage hiring, performance, and career development.
- Communicate technical concepts, trade-offs, and results clearly to executives and non-technical stakeholders.
- Manage delivery against budgets, timelines, and quality standards, and apply agile methods appropriately.
- Lead the design and delivery of data and AI solutions, including NLP, forecasting and time series, anomaly detection, and generative AI, for use cases across market data, trading, risk, corporate functions, and operational domains.
- Guide the responsible use of generative AI and LLM-based solutions, including RAG, agentic workflows, prompt and model evaluation, vector search, and guardrails.
- Oversee the full AI/ML lifecycle: experimentation, training, evaluation, deployment,



monitoring (including drift and performance), and retirement.
- Establish MLOps practices such as model registries, feature management, reproducible pipelines, and automated validation and release.
- Oversee data modelling, warehousing and lakehouse architecture, and streaming, ensuring the data layer is ready to serve both analytics and AI workloads.
- Make build-versus-buy and platform-fit decisions across AWS and Snowflake services for data and AI workloads, balancing performance, cost, and maintainability.
- Drive adoption of modern engineering practices across data and AI, including CI/CD, infrastructure as code, automated testing, code review, and observability.
- Establish and uphold standards for model risk management, data quality, lineage, privacy, and responsible AI.
- Promote transparency, explainability, and fairness in AI-driven decisions.
- Partner with Information Security, Legal, Compliance, and Risk to ensure solutions meet regulatory, contractual, and internal policy requirements.
- Work across business lines, enterprise architecture, and technology teams to align on platforms and standards.
- Evaluate and manage technology vendors, cloud services, and partnerships.
- Own production support for the AI and data applications, pipelines, and platform components the team delivers,



including incident response, root cause analysis, and problem management.
- Define and meet service levels (SLAs/SLOs) for availability, data freshness, latency, and model performance, and report on them to business partners.
- Establish and run an operating model for support, including on-call rotations, escalation paths, runbooks, and handover between delivery and run teams.

Required Qualifications

Leadership and Experience

- Degree in Computer Science, Software Engineering, Applied Mathematics, or a related quantitative field.
- 8+ years of experience in data, AI/ML, or software engineering, including 3+ years leading technical teams or managing managers.
- Proven record of delivering both data products and AI/ML products into production at scale.
- Robust stakeholder management, with the ability to influence senior leaders and translate business needs into technical solutions.
- Strong communication skills, with the ability to translate AI and data work into business impact.

AI Expertise

- Production experience delivering ML or AI solutions, including at least one of NLP, time series, or computer vision.
- Hands‑on or deep working knowledge of generative AI and LLM application patterns (RAG, agents, evaluation, guardrails) and a track record of taking at least one such solution to production.
- Solid understanding of machine learning and statistics (supervised and unsupervised learning, deep learning, model evaluation).
- Experience with MLOps practices, including model deployment, monitoring, and lifecycle management.
- Familiarity with ML frameworks and tooling such as PyTorch, TensorFlow, scikit-learn, and M

📌 Senior Manager, AI and Data (Toronto)
🏢 TMX Group
📍 Toronto