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.
Proven record of delivering data and AI/ML products into production at scale.
Strong stakeholder management and ability to influence senior leaders.
Production experience with ML or AI solutions, including NLP, time series, or computer vision.
Hands-on knowledge of generative AI and LLM application patterns with a track record of production deployment.
Advanced SQL and strong Python skills, with experience in performance tuning on large datasets.
Responsibilities
Define and execute an integrated AI and data roadmap with business and technology leaders.
Build and retain a high-performing, diverse team in data and AI engineering.
Communicate technical concepts clearly to executives and non-technical stakeholders.
Manage delivery against budgets, timelines, and quality standards using agile methods.
Lead the design and delivery of data and AI solutions across various domains.
Guide the responsible use of generative AI and LLM-based solutions.
Oversee the full AI/ML lifecycle from experimentation to retirement.
Benefits
Opportunity to lead a diverse and high-performing team.
Engagement with cutting-edge AI and data technologies.
Collaboration with senior leaders and cross-functional teams.
Focus on responsible AI and ethical data practices.
Access to modern engineering practices and tools.
Full Job Description
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.
