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Added 6 mins ago
DI
FinOps Specialist – Azure Data Cost Analytics (1884) $70-72/hr
Direct IT Recruiting Inc.
Toronto, ON
- $70 to $72 CAD / hour
- Contract
SKILLS: 5+ years FinOps, FOCP FinOps Certified Practitioner, Azure Cloud Data Cost Analytics, FOCUS FinOps Open Cost and Usage Specification, reports, dashboards, Python, Power BI, SQL,…
Added 6 mins ago
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TMX Group

Senior Manager, AI and Data

Toronto, ON, Canada

$165,000 to $175,000 CAD / year · Full-time

Apply About employer

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Job details

Pay: $165,000 to $175,000 CAD / year
Job type: Full-time
Apply by: Nov 25, 2026
Posting language: English
Posted: Oct 26, 2026

Original job description

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.
Strong 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 MLflow.
Familiarity with model risk management and responsible AI frameworks.

Data Expertise

Advanced SQL and strong Python skills, with experience in performance tuning on large datasets.
Strong experience with Snowflake, including virtual warehouses, Snowpipe, Streams and Tasks, RBAC and masking policies, and cost management.
Hands-on experience with core AWS data and security services (S3, IAM, KMS, Glue, Lambda, Kinesis or MSK).
Experience with data architecture, data modelling, data warehousing and lakehouse patterns, and streaming technologies, including open table formats such as Apache Iceberg.
Experience with data tooling such as Spark, Kafka, Airflow, and dbt.
Familiarity with data governance, quality, lineage, and privacy frameworks.

Platform and Tooling (Shared)

Experience with cloud platforms (AWS, Azure, or GCP) and containerization/orchestration (Docker, Kubernetes).
Proficiency with CI/CD pipelines, Git-based workflows, and infrastructure as code (e.g., Terraform).
Familiarity with monitoring, logging, and alerting for production systems, including SLOs and incident response.

Qualities

Strategic thinking and business acumen
People leadership and talent development
Technical credibility and sound judgment
Accountability and results orientation
Collaboration and influence without authority
Integrity and commitment to responsible AI

Salary Range: $165,000 - $175,000/year CAD. Please note that the salary range included is a guideline only. The salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.

The recruiting efforts for this role are intended to fill a vacant position.

In the market for…

Excitement - Explore emerging technology and innovation, as well as ventures and digital finance that shape the future of global markets! Experience the movement of the market while grounded in the stability of close to 200 years of success.

Connection - With site hubs in some of the world’s most multicultural cities, we leverage our size and structure to create rich connections and belonging while experiencing powerful global impact through our work.

Impact - More than a platform, we use our talents to power mission-critical systems that drive global economic advancement, innovation, and growth. As well, our employee-led Team Impact spreads social good via our giving strategy.

Wellness - From empathetic leadership to a culture of flexibility and balance, we believe wellness at work creates the maximum yield and a stronger “we”. Plus, with a cloud-first and hybrid workstyle, as well as generous time-off and leaves, we support a life well lived!

Growth - From a growth mindset in our work, to expansion in our business, TMX is home to action-takers energized by the achievement of ambitious growth.

Ready to enrich your career with impactful work, leaders who truly care, and the flexibility and programs to help you thrive as part of #TeamTMX ? Apply now.

Please note that our company is not currently sponsoring work permit applications and the applicant must be authorized to work in the country where this position is located.

TMX is committed to creating and sustaining a collegial work environment in which all individuals are treated with dignity and respect and one which reflects the diversity of the community in which we operate. We provide accommodations for applicants and employees who require it.

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More about this job

Responsibilities

Define and deliver the AI and data roadmap, lead a multidisciplinary engineering team, and oversee production AI/ML and data solutions across the organization. Establish responsible AI, governance, MLOps, and production support practices while managing delivery, platforms, vendors, and service levels.

Requirements

Requires a degree in computer science, software engineering, applied mathematics, or a related quantitative field, with 8+ years of relevant experience and 3+ years leading technical teams or managers. Candidates need a proven record delivering production data and AI products, strong expertise in AI/ML and generative AI, and hands-on knowledge of data platforms, cloud services, engineering practices, and production operations.

Skills

AI Strategy
People Leadership
Machine Learning
Generative AI
Large Language Models
MLOps
Python
SQL
Snowflake
AWS
Data Architecture
Data Engineering
Data Governance
Responsible AI
CI/CD
Incident Response

Benefits

Flexible Workstyle
Generous Time Off
Leaves
Wellness Programs

Visa sponsorship

Not detected in the job text

Keywords

Artificial Intelligence
Machine Learning
Generative AI
Large Language Models
Retrieval-Augmented Generation
Agentic Workflows
Natural Language Processing
Time Series Forecasting
Anomaly Detection
MLOps
Python
SQL
Snowflake
AWS
S3
IAM
KMS
Glue
Lambda
Kinesis
Apache Iceberg
Spark
Kafka
Airflow
dbt
PyTorch
TensorFlow
scikit-learn
MLflow
Docker

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