About the Role: The AI Platform Engineering Leadis the senior technical authority for how AI is built at AGF.

Reporting to the VP, Technology Services, the role defines the blueprints that AI solutions follow, owns the platforms those solutions run on, and provides the engineering environment, tooling, and guardrails that allow AI teams to move quickly without compromising security, cost control, or compliance.

The role works closely with AI Forward Deployed Engineers and other technical teams to connect AI workloads to governed enterprise data, ensuring every deployed agent and model is identified, permissioned, monitored, and auditable.

Platform scope include but are not limited to Databricks, Microsoft Copilot Studio, Azure AI Foundry, Anthropic Claude, and Microsoft Agent 365.

This is a hands-on role.

The successful candidate will spend meaningful time in code, configuration, and architecture, designing reference implementations, building the first version of shared components, and resolving integration and access issues, while also setting standards, mentoring other engineers, evaluating platforms, and advising senior leadership.

It is a senior role carrying technical leadership responsibility, intended for an engineer-architect who is energized by building a new capability from a blank page and who is comfortable making decisions with incomplete information in a fast-moving technology landscape.

Why Join AGF? This is a unique opportunity to help shape AGF''s AI future from the ground up.

You will have the opportunity to influence strategy, drive business outcomes, build enterprise-scale solutions, and help establish AI as a core capability across AGF.

Your Responsibilities: AI Architecture & Blueprints: Define and maintain the reference architecture for AI and agentic solutions at AGF, covering agent design patterns, retrieval-augmented generation, orchestration, memory and state, tool and API access, model selection, and human-in-the-loop controls.

Produce blueprints, reference implementations, and decision guides that engineering teams can apply directly, rather than architecture documents that require interpretation.

Establish platform and model selection criteria, and make clear recommendations on where each platform is the right choice and where it is not.

Maintain a forward-looking roadmap for AI platforms, assessing new capabilities, deprecations, and vendor direction, and translating them into a practical plan for AGF.

Lead architecture and design reviews for AI solutions built by the AI Engineers and business teams.

Set the target state for AI solution cost, performance, latency, and resilience, and design to it. AI Platform Ownership & Engineering: Own the technical configuration, evolution, and operational health of AGF''s AI platforms, including Databricks, Azure AI Foundry, Microsoft Copilot Studio, and Anthropic Claude.

Evaluate, pilot, and onboard new AI platforms, models, and tooling across commercial, hosted, and open-source options.

Manage model access, entitlements, quotas, regions, and data residency configuration across providers.

Implement gateway, routing, caching, and rate-limiting patterns that control consumption cost and give engineers a consistent interface to model providers.

Establish cost transparency for AI consumption, with monitoring, budgets, and alerting by team and by use case.





Provide observability for AI workloads: tracing, prompt and response logging, quality and drift monitoring, usage analytics, and incident diagnostics.

Own the engineer onboarding experience so that a new developer can be productive in days rather than weeks.

Design and operate the promotion process for AI assets, including prompts, agents, models, indexes, notebooks, and applications, through development, test, and production.

Implement CI/CD pipelines and infrastructure as code for AI workloads, with automated testing, approval gates, and rollback.

Define workplace strategy, workspace structure, and separation of duties consistent with AGF''s change management and audit requirements.

Ensure AI solutions meet enterprise standards for availability, monitoring, alerting, support handover, and disaster recovery.

Work with Technology Services operations teams to bring deployed AI solutions into established support and incident management processes.

Define how AI workloads consume governed data, including lakehouse patterns, Unity Catalog governance, vector and feature stores, embedding pipelines, and lineage.

Provide the engineering path that moves a promising prototype onto governed, production-grade data.

Implement agent governance and lifecycle management covering registration, ownership, entitlement, monitoring, retention, and decommissioning Provide expert technical guidance on complex projects, foster a culture of innovation, and elevate the team''s capabilities in building robust AI solutions.

Act as the technical counterpart to the AI Engineers, run an AI engineering community of practice.

Build and lead a small AI platform engineering team as adoption scales.

Your Qualifications: Bachelor''s degree in Computer Science, Engineering, Mathematics, Data Science, Information Technology, or a related discipline.

Minimum 7 years of experience in software engineering, platform engineering, data engineering, or solution architecture Minimum 3 years of hands-on experience designing and delivering generative AI, agentic, or machine learning solutions Demonstrated ownership of a shared engineering platform or developer environment used by multiple teams.

Experience establishing engineering standards, CI/CD, and release governance in a regulated or audited environment.

Track record of building a capability from the ground up, including tooling selection, vendor evaluation, and first-of-kind implementations.

Experience partnering with security, risk, and compliance functions to bring new technology into controlled production use.

Experience operating in fast-paced environments with evolving priorities.

Financial services, investment management, wealth management, or capital markets experience is considered a strong asset.

Technical Skills: Required: Azure architecture and services, including Azure AI Foundry, Entra ID, networking, and key and secret management.

Databricks, including lakehouse architecture, Unity Catalog governance, jobs,



and workflows.

Microsoft Copilot Studio and the Microsoft 365 and Power Platform extensibility model.

Anthropic Claude and other frontier model APIs, including their enterprise deployment and safety controls.

Azure Dev

Ops, Git

Hub, CI/CD pipelines, infrastructure as code such as Terraform or Bicep, and modern Dev

Ops practice.

Strong hands-on software engineering with advanced Python, and working knowledge of at least one additional language relevant to enterprise integration.

Agent frameworks and orchestration, tool and function calling, and multi-agent design patterns.

Experience with Microsoft SQL server and related database technology Retrieval-augmented generation, embeddings, vector search, and context engineering at enterprise scale.

Prompt engineering, model evaluation, and AI testing and observability tooling.

Application and data security architecture, including secrets management and data classification.

Preferred: MLOps and LLMOps tooling, and model lifecycle management.

Knowledge graphs, semantic layers, and metadata management.

Containerized and cloud-native application delivery, including Kubernetes and serverless patterns.

Fin

Ops practices applied to cloud and AI consumption.

Relevant certifications such as Azure Solutions Architect, Databricks Data Engineer or Architect Familiarity with investment management, trading, research, or client servicing platforms and data.

Microsoft Agent 365, Entra Agent ID, and Microsoft Purview.

Leadership Competencies: Exceptional problem-solving and analytical abilities.

Robust stakeholder management and relationship-building skills.

Ability to influence and drive outcomes without direct authority.

Strong communication and presentation skills.

Product-oriented mindset focused on measurable business outcomes.

Curiosity, adaptability, and a passion for continuous learning.

Ability to operate effectively in ambiguous and rapidly evolving environments.

Sound judgment balancing innovation with governance, security, and risk considerations.

Success Measures: Reliability, security, and compliance of AI solutions in production, with no material audit or risk findings.

Engineer productivity and satisfaction with the AI development environment.

Contribution to the growth, maturity, and credibility of AGF''s AI capability.

Business value delivered through deployed AI solutions.

Contribution to revenue-generating and growth-enabling initiatives.

Operational stability, reliability, and maintainability of solutions.

Reuse of developed components and engineering patterns.

Contribution to the growth and maturity of AGF''s AI capability.

Compensation: The anticipated compensation range for this role is $130,000 $180,000 annually, which represents base salary and, where applicable, variable compensation components (e.g., annual bonus, commissions, etc.).

Actual compensation will be determined based on many factors such as role location, candidate experience / qualifications, market conditions, and internal equity. AGF aims to offer a comprehensive and competitive total rewards package designed to support the success and well-being of our employees, which may include a combination of base salary, variable compensation, benefits, and retirement savings plans. #LI-JF1 #LI-HYBRID

📌 AI Platform Engineering Lead (Toronto)
🏢 AGF Investments
📍 Toronto