AI Engineer in Toronto

We are seeking a highly skilled AI Engineer to lead the design, solutioning, and development of scalable, reusable AI-driven core capabilities for our global Finance Transformation program. This role sits at the intersection of finance, technology, and governance, enabling the development of intelligent, compliant-by-design solutions that support key Finance functions at a global level.

You will partner closely with cross-functional teams including Finance, IT, Global Analytics, Legal, Compliance, and Risk to deliver secure, scalable, and regulatory-aligned AI solutions across the enterprise.

MAJOR RESPONSIBILITIES:

• Lead the architecture, design, and development of AI/ML solutions at a global level to support finance and controllership processes (e.g., close, reconciliation, reporting, anomaly detection etc).
• Build scalable, reusable core AI capabilities that can be leveraged across multiple finance use cases and geographies.
• Translate business requirements into robust technical solutions, ensuring alignment with enterprise architecture and data strategy.
• Collaborate with Finance, IT, Data & Analytics, Legal, Compliance, Security, Enterprise Architecture and Risk teams to design and implement compliant-by-design AI solutions.
• Ensure adherence to regulatory requirements, data privacy laws, and internal governance frameworks.
• Develop and deploy models using modern AI/ML frameworks; ensure model performance, monitoring, and lifecycle management.
• Identify opportunities to automate and optimize finance processes using AI (e.g., intelligent automation, NLP, predictive analytics).
• Provide technical leadership and mentorship to junior engineers and cross-functional teams.
• Ensure high quality code that meets business objectives, quality standards and development guidelines.
• Build reusable pipelines, processes, and tools to streamline LLM and generative AI workflows while driving adoption of MLOps best practices, including CI/CD pipelines, versioning, testing, and model governance.
• Manage project stakeholder expectations and issue communications on progress.
• React to shifting priorities without compromising deadlines and momentum.
• Stay current with emerging AI technologies and assess their applicability within finance and risk-controlled environments.

QUALIFICATIONS:

• Must have: 2 - 5 years’ experience in AI Engineering and/or Machine Learning (ML) with a focus on LLMs, with deep expertise in writing, and reviewing production code in Python.
• Understanding the development lifecycle for LLMs— developing data sets for pre-training, instruction tuning, and preference alignment alongside the modelling techniques for each stage and LLM deployment is a major plus.
• Strong knowledge of LLM frameworks and libraries (such as transformers, trl, deepspeed, PyTorch), and exposure to various ML techniques and their practical implementation in production at large scale.
• Experience building and deploying solutions on cloud platforms (AWS, Azure, or GCP).
• Experience on distributed, high throughput and low latency architectures.
• Strong fundamentals in NLP techniques for text representation, semantic extraction techniques, data structures and modeling.
• Experience building software on top of major container technology (Kubernetes, Docker etc.).
• Knowledge of version control using Jenkins, GitHub Actions, GitLab CI, Jenkins, or Azure DevOps.
• Solid understanding of data engineering concepts and working with large-scale datasets.
• Experience implementing ML Ops practices and production-grade AI systems.
• Familiarity with data privacy, model governance, and responsible AI principles.

Nice to have:

• Experience and Knowledge of Finance Domain: Understanding of finance concepts, workflows, or platforms is a strong asset for this role.
• Knowledge of financial processes such as close, consolidation, reporting, and audit.
• Exposure to regulatory and compliance frameworks (e.g., SOX, GDPR, model risk management).
• Experience with ERP systems (e.g., SAP, Oracle) and finance data ecosystems.
• Experience defining system architecture and exploring technical feasibility tradeoffs is a plus.
• Strong understanding of AI risk, explainability, and auditability.
• Familiarity with end-to-end application development using full stack is a plus.
• Experience in P&C insurance is a plus.
• Strong problem-solving and analytical thinking.
• Ability to work across cross-functional and global teams.
• Excellent communication and stakeholder management skills.
• High attention to governance, risk, and compliance considerations.
• Ability to balance innovation with control and scalability.

What Success Looks Like:

• Delivery of scalable AI capabilities embedded within finance processes.
• Measurable improvements in key KPIs - efficiency, accuracy, and compliance.
• Strong adoption of AI solutions across finance teams globally.
• Robust governance and audit-ready AI implementations.