Senior Applied AI Engineer – GenAI & Actuarial Systems

Manulife group; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

Manulife’s Group Functions AI team is scaling AI and advanced analytics capabilities across Finance, Treasury, Actuarial, and related enterprise functions to improve how decisions are made and how insights are generated. This role focuses on building solutions that use machine learning, GenAI, and modern analytical approaches to solve business problems at enterprise scale.

In this role, you will take business problem contexts and translate them into AI use cases such as predictive modeling,

Responsibilities

• Translate business problems into a clear AI use‑case approach with data flows, modeling, evaluation, and governance.
• Apply strong design thinking: clarify user needs, define decision points, and plan adoption.
• Create lightweight, high‑quality design artifacts (system context, data lineage, decision log).
• Make design trade‑offs between accuracy, explainability, robustness, and operational sustainability.
• Develop models for predictive risk, segmentation, anomaly detection, and forecasting.
• Build GenAI capabilities like retrieval, structured summarization, and guided workflows.
• Coordinate multi‑step agentic workflows with traceability and controls.

Job description

Manulife’s Group Functions AI team is scaling AI and advanced analytics capabilities across Finance, Treasury, Actuarial, and related enterprise functions to improve how decisions are made and how insights are generated. This role focuses on building solutions that use machine learning, GenAI, and modern analytical approaches to solve business problems at enterprise scale.

In this role, you will take business problem contexts and translate them into AI use cases such as predictive modeling, segmentation, anomaly detection, scenario analysis, and automation of analytical workflows. The emphasis is on building reusable, production-ready components that integrate into business workflows, with clear explainability, strong evaluation, ongoing monitoring, and governance-ready evidence.

Position Responsibilities:

You will work closely with business stakeholders and engineering partners to deliver solutions that are explainable, robust, and operationally sustainable—helping accelerate decision cycles, improve consistency, and enable teams to focus on higher-value judgment where it matters.

Own end-to-end solution design for actuarial AI

• Translate business problems into a clear solution approach: business workflow, data flow, modeling approach, evaluation plan, and operational controls.
• Apply strong design thinking: clarify user needs, define decision points, design for adoption, and make trade-offs explicit.
• Create lightweight, high-quality design artifacts (e.g., system context, runtime sequence, agent/tool map where applicable, data lineage, decision log) that make build and governance straightforward.
• Make smart design trade-offs: accuracy vs explainability, robustness vs speed, and model complexity vs operational sustainability.

Build strong ML, GenAI, and agentic capabilities for actuarial use cases

• Develop models such as predictive risk and behavior models, forecasting and scenario models, segmentation, anomaly detection, and optimization approaches.
• Build GenAI capabilities such as retrieval-based solutions, structured summarization/extraction, and guided analytical workflows to accelerate insight generation.
• Where applicable, design agentic workflows that coordinate multiple steps and tools (e.g., retrieval, calculations, rules, and structured outputs) while maintaining traceability and controls.
• Engineer features from large structured and unstructured datasets and ensure solutions remain stable as data and assumptions evolve.

Set a high bar for evaluation and evidence

• Define performance expectations with stakeholders and implement out-of-time testing, backtesting, error analysis, stability checks, and sensitivity analysis.
• For GenAI and agentic workflows, design practical evaluation: scenario coverage, edge cases, human review rubrics, quality scoring, and regression testing.
• Document model limitations clearly and build guardrails that ensure outputs are used appropriately.

Partner closely to productionize and operate solutions

• Collaborate with data engineering, ML engineering, and software teams to productionize: 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