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, robust 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**
Produce documentation and evidence required for model risk review, including assumptions, validation results, monitoring plans, and UAT evidence.

Ensure privacy and 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 solutions using reusable templates, checklists, and examples to improve consistency and delivery speed.
**Required Qualifications:**
6–10 years of experience in applied data science, machine learning, or advanced analytics, with demonstrated end-to-end delivery into production beyond notebooks, including support for UAT and post-launch iteration.

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

Hands-on experience with modern DS/ML tooling such as scikit-learn, PyTorch or Tensor

Flow, and distributed processing platforms such as Spark or Databricks, including feature engineering and model development at scale.

Demonstrated ability to design and communicate solution architecture: produce clear diagrams and short specs covering data flow, runtime flow, interfaces, dependencies, failure modes, and operational controls; align stakeholders on trade-offs and scope.

Strong evaluation skills across ML and advanced analytics: backtesting or out-of-time testing, metric selection, error analysis, stability testing, and sensitivity analysis; ability to translate evaluation into business-ready acceptance criteria.

Experience building and operating monitored solutions: data quality checks, drift detection, performance deterioration monitoring, alerting, and practical remediation approaches.

Strong communication and stakeholder management: ability to explain outputs, limitations, uncertainty, and design decisions in plain language, and drive adoption in business workflows with domain partners.

Working knowledge of GenAI and agentic patterns, including when they add value and how to deploy them responsibly; experience contributing to at least one GenAI-enabled capability such as retrieval-based solutions, structured summarization/extraction, or tool-using workflows.
**Preferred or Nice to have:**
Experience delivering solutions in governed environments, including documentation, validation evidence, monitoring plans, UAT support, and approvals.

Experience with GenAI patterns such as retrieval-based solutions, structured outputs, tool/function calling, and agentic workflows, along with practical evaluation methods.

Familiarity with vector search and embeddings, semantic retrieval, and orchestration frameworks used to build production GenAI systems.

Experience implementing GenAI guardrails including accuracy controls, safe output formatting, data minimization, access controls, and human review workflows.

Ability to influence and mentor others through design reviews, code reviews, and evaluation practices without formal people management responsibility.
**When you join our team:**
We'll empower you to learn and grow the career you want.

We'll recognize and support you in a flexible environment where well-being and inclusion are more than just words.

As part of our global team, we'll support you in shaping the future you want to see.
#LI-Hybrid
The role being advertised is an existing vacancy.
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**Referenced Salary Location**
Toronto, Ontario
**Modalidades de Trabajo**
Híbrido
**Salary range is expected to be between**
$129,400.00 CAD - $179,400.00 CAD
Los empleados también tienen la oportunidad de participar en programas de incentivos y recibir una compensación vinculada al rendimiento empresarial e individual.

El salario real variará según las condiciones del mercado local, la geografía y factores relacionados con el puesto como conocimientos, habilidades, requerimientos, experiencia y educación/formación.

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📌 Senior Applied AI Engineer – GenAI & Actuarial Systems (Toronto)
🏢 Manulife
📍 Toronto