Applied AI Engineer – GenAI Systems

Manulife is making a significant investment in Advanced Analytics and GenAI to transform how Finance, Treasury, and Actuarial teams make decisions! Our AI team builds practical, governed solutions that move from idea to implementation and are adopted in real business workflows.

We’re hiring two Applied AI Engineers with strong modeling skills, solution development experience, and a product outlook. They will build AI and GenAI capabilities that integrate into real business workflows. These solutions must be reliable for engineers to implement, easy for collaborators to understand and use, and clear for governance teams to review with evidence, testing, and controls. If you enjoy turning ambiguous business problems into clear system designs, strong evaluations, and reliable production outcomes, this role is for you!

Position Responsibilities:

Own end-to-end solution design (GenAI + ML)

Translate business problems into a clear solution approach: user workflow, data flow, model approach, evaluation plan, and operational controls.

Create lightweight, high-quality design artifacts (e.g., system context, runtime sequence, agent/tool map, data lineage, decision log) that make build and governance straightforward.

Make smart design trade-offs: accuracy vs explainability, cost vs performance, speed vs robustness.

Build strong models and GenAI components for Finance & Actuarial use cases

Develop ML solutions such as forecasting, classification, NLP, optimization, anomaly detection, and scenario analysis.

Build GenAI capabilities such as retrieval-based solutions (RAG), structured summarization, transaction understanding, variance explanations, and tool-using workflows (where applicable).

Engineer features from structured + unstructured data and ensure solutions remain stable as data evolves.

Set a high bar for evaluation and evidence

Define performance expectations with collaborators and implement backtesting / out-of-time testing and error analysis.

For GenAI, design practical evaluation: scenario coverage, edge cases, human review rubrics, quality scoring, and regression testing.

Document model limitations clearly and build guardrails for safe use.

Partner closely to product ionize and operate solutions

Collaborate with Data Engineering, ML Engineering, and Software teams to product ionize: data pipelines, model packaging, CI/CD, deployment, and monitoring.

Implement monitoring for data quality, drift, performance deterioration, and operational failures; define remediation actions when thresholds breach.

Contribute to runbooks and support adoption and UAT with business users.

Work in a governed environment

Produce the documentation and evidence required for model risk review (assumptions, validation results, monitoring plan, UAT evidence, and approvals).

Ensure privacy/security expectations are met through data minimization, appropriate access controls, and safe handling of sensitive information.

Raise team capability

Mentor junior scientists through design reviews, code reviews, and evaluation practices.

Help standardize “how we build” (templates, checklists, examples) so delivery becomes faster and more consistent.

Required Qualifications:

4–7 years of experience in applied data science / machine learning, with demonstrated end-to-end delivery into production (beyond notebooks), including support for UAT and post-launch iteration.

Strong Python + SQL, with solid software engineering practices:
Git-based workflows, code reviews, unit/integration testing, logging, readable code structure, and basic performance tuning.

Hands‑on experience with modern DS/ML tooling (e.g., scikit‑learn, PyTorch/Tensor Flow, Spark/Databricks or similar), including feature engineering and model development at scale.

Demonstrated ability to…