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 transparent 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 productionize and operate solutions**
Collaborate with Data Engineering, ML Engineering, and Software teams to productionize: 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 build and communicate solution architecture.

Create clear diagrams and concise specs that include data flow, runtime flow, interfaces, failure modes, and operational controls.

Align collaborators on trade-offs and scope.

Experience building and evaluating GenAI solutions, including at least one of: RAG, structured summarization/extraction, classification with LLMs, tool/function calling, or agentic workflows (multi-step orchestration with tools/data stores).

Strong evaluation skills across ML and GenAI: backtesting/holdouts, metric selection, error analysis, and quality evaluation frameworks for GenAI (scenario coverage, edge cases, human review rubrics, regression tests).

Understanding of production readiness: monitoring for data quality and drift, performance deterioration, cost/latency considerations for GenAI, and practical remediation approaches.

Strong communication and collaborator management: ability to explain outputs, limitations, uncertainty, and build decisions in plain language and drive adoption in business workflows.
**Preferred / Nice to have:**
Hands-on GenAI experience across multiple patterns: RAG, prompt orchestration, structured outputs, tool/function calling, agentic workflows (multi-step reasoning with tools), and practical evaluation approaches.

Experience designing GenAI systems beyond the model: retrieval design, grounding strategies, prompt/version management, caching, and cost/latency trade-offs.

Experience with cloud-based data/ML stacks and production patterns: model registry, CI/CD, monitoring, APIs/microservices, and automation for retraining or refresh cycles.

Familiarity with modern GenAI engineering components such as vector databases, embedding strategies, semantic search, and orchestration frameworks (e.g., Semantic Kernel / Lang

Chain-style frameworks).

Experience working in Finance, Treasury, Insurance, IFRS-17, or Actuarial environments and/or within model governance practices (documentation, validation evidence, monitoring plans).

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

Strong solution design influence: ability to mentor peers through design reviews, code reviews, and evaluation practices (without formal people leadership).
**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.
**Acerca de Manulife y John Hancock**
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http://www.manulife.com .
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**Referenced Salary Location**
Toronto, Ontario
**Modalidades de Trabajo**
Híbrido
**Salary range is expected to be between**
$94,430.00 CAD - $144,430.00 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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📌 Applied AI Engineer – GenAI Systems (Toronto)
🏢 Manulife
📍 Toronto