IT Manager Development (Python)

TD•Toronto, ON

8d•CA$96,900 - CA$136,800•Onsite

About The Position

The Pricing technology team is seeking an experienced IT Manager to lead the design and delivery of an AI-enabled capability that integrates with existing models and creates a scalable, business-facing layer for scenario forecasting, A/B testing, model output review, and production workflow integration. This role will be accountable for providing hands-on technical leadership across Python-based model integration, API/service design, data and model orchestration, validation workflows, user interaction design, and enterprise-ready implementation.

Requirements

Post-secondary degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical discipline, or equivalent practical experience.
Strong hands-on experience with Python, including reusable package design, API integration, automation, data processing, orchestration, and production-quality coding practices.
Proven experience designing and delivering integration layers for models, analytics engines, APIs, microservices, data pipelines, or workflow automation solutions.
Practical understanding of AI/ML solution patterns, including model consumption, input/output handling, evaluation, monitoring, validation, explainability, and responsible AI controls.
Experience translating business needs into technical designs, user stories, integration patterns, acceptance criteria, and delivery plans.
Strong knowledge of enterprise technology delivery practices, including Agile delivery, SDLC, DevOps, CI/CD, testing automation, release management, monitoring, resiliency, and production support.
Ability to assess architecture options, technical trade-offs, risks, dependencies, data requirements, scalability considerations, and downstream operational impacts.
Demonstrated ability to lead cross-functional teams and influence stakeholders across technology, business, data, architecture, risk, security, compliance, and operations.
Excellent communication skills with the ability to explain complex AI and integration concepts to both technical and non-technical audiences.

Nice To Haves

Experience in banking, financial services, pricing, forecasting, analytics, or decisioning platforms.
Familiarity with cloud-enabled AI services, Azure AI, Azure OpenAI, Databricks, SQL, REST APIs, Git, CI/CD pipelines, monitoring tools, Jira, Confluence, and enterprise data platforms.
Experience building business-facing tools for scenario analysis, simulation, experimentation, dashboarding, workflow review, or decision support.
Understanding of model governance, auditability, privacy, data lineage, access controls, and regulated production environments.
Ability to operate with ambiguity, structure complex delivery work, remove blockers, and drive measurable business and technology outcomes.

Responsibilities

Lead the technical design and implementation of an AI capability that integrates with existing enterprise models and enables business users to interact with model outputs in a controlled, intuitive, and scalable manner.
Design and develop a Python wrapper layer to standardize model integration, manage inputs and outputs, support reusable interfaces, and simplify future onboarding of additional models.
Build or guide the development of APIs, services, orchestration workflows, and integration patterns that connect models with business applications and production systems.
Develop an interactive business layer that enables scenario forecasting, what-if analysis, A/B testing, model output review, and validation before downstream execution.
Partner with business stakeholders, product owners, data science teams, architects, QE, DevOps, risk, security, and operations teams to translate business outcomes into executable technical solutions.
Define technical requirements, solution options, delivery milestones, dependencies, risks, controls, and acceptance criteria for AI-enabled capabilities.
Establish validation workflows to ensure model outputs are reviewed, explainable, traceable, and approved before integration into production processes.
Ensure the solution aligns with enterprise architecture, data governance, security, privacy, responsible AI, model governance, release management, and operational readiness standards.
Provide technical guidance to engineering teams on clean Python design, modular architecture, API-first integration, automated testing, observability, resiliency, and production supportability.
Drive issue resolution, performance tuning, defect triage, release readiness, and post-implementation stabilization for AI-enabled workflows.
Prepare clear technical documentation, implementation plans, executive updates, and stakeholder communications to support delivery transparency and decision-making.
Mentor developers and analysts, promote engineering excellence, and foster a culture of innovation, accountability, and responsible adoption of AI capabilities.