Artificial Intelligence Solutions Engineer

Manulife•Montreal, QC

1d•CA$113,260 - CA$210,340•Hybrid

About The Position

The AI Solutions Engineer works alongside GWAM business teams to find where AI and agentic tooling can remove effort, shorten cycle time or improve quality in an existing business process — and then engineers, ships and measures that solution end to end. The role is deliberately a consumer and integrator of AI capability: value is created by applying and managing tooling and capabilities to real business workflows quickly and safely.

Requirements

10+ years of experience, including three years applying AI or LLM tooling in a production setting
Bachelor's degree in Computer Science, Computer Engineering, Computational Science or equivalent.

Nice To Haves

Experience in financial services, wealth or asset management

Responsibilities

Partner with business and technology stakeholders to identify, qualify and prioritise processes where AI tooling produces a measurable business outcome — and to rule out those where it does not.
Translate a business problem into a working AI-enabled solution design, spanning the workflow, the data it touches, the tooling applied and the human checkpoints retained.
Build and integrate solutions using commercial and platform AI tooling — LLM APIs, agent frameworks, orchestration and prompt engineering — against existing systems of record.
Deploy solutions to production using containerised delivery practices, meeting enterprise security, privacy and control standards for a regulated financial services environment.
Instrument every solution for business outcome measurement, and iterate based on what the measurement shows rather than on assumed benefit.
Apply and uphold responsible-AI guardrails — human oversight, auditability, data handling and model-risk requirements — in partnership with risk and compliance functions.
Document and hand solutions to run-state ownership, and feed reusable patterns back to the platform and architecture teams.
Coach business and technology colleagues on the practical application of AI tooling, raising the enablement capability of the wider team.