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 Associate Applied AI Engineers who bring strong technical depth, sound software engineering practices, and a modern GenAI design mindset to help deliver AI and GenAI capabilities that integrate effectively into real business workflows. This is an excellent opportunity for candidates with graduate-level training in AI/ML or related quantitative disciplines, as well as those in early-career applied AI roles, who want to work on meaningful business problems rather than research prototypes or notebook-based analysis alone. If you enjoy taking ambiguous business challenges and translating them into structured solution designs, measurable experiments, and production-ready outcomes, this role is for you.

Position Responsibilities: 

1. Contribute to end-to-end solution design (GenAI + ML)

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

Create lightweight, high-quality design artifacts such as system context, runtime sequence, agent/tool maps, data lineage, and decision logs that support implementation and governance.

Participate in design discussions and make thoughtful trade-offs across accuracy, explainability, cost, latency, and maintainability.

2. Build models and GenAI components for Finance & Actuarial use cases

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

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

Engineer features from structured and unstructured data and help ensure solutions remain robust as data evolves.

3. Apply strong evaluation and testing practices

Implement performance evaluation using holdouts, backtesting, error analysis, and fit-for-purpose metrics aligned to the business problem.

For GenAI, help design practical evaluation approaches such as scenario coverage, edge cases, human review rubrics, quality scoring, and regression testing.

Document model limitations clearly and support guardrails that improve the reliability and safe use of outputs.

4. Partner closely to productionize and operate solutions

Collaborate with Data Engineering, ML Engineering, and Software teams to productionize solutions through reliable data pipelines, model packaging, CI/CD, deployment, and monitoring.

Write maintainable, tested code using strong software engineering practices such as version control, modular design, logging, and code review.

Support monitoring for data quality, drift, performance deterioration, and operational failures, and help investigate issues when thresholds are breached.

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

5. Work in a governed environment

Contribute to the documentation and evidence required for model risk review, including assumptions, validation results, monitoring plans, UAT evidence, and approvals.

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

Follow established standards for reproducibility, traceability, model documentation, and auditability.

6. Grow team capability and delivery maturity

Learn quickly from design reviews, code reviews, and stakeholder feedback, and apply those lessons to future work.

Contribute reusable components, templates, examples, and testing patterns that make team delivery faster and more consistent.

Stay current with emerging AI and GenAI engineering patterns and bring forward practical ideas that improve how the team builds solutions.

Required Qualifications:

• Master’s or PhD in Computer Science, Statistics, Machine Learning, Applied Mathematics, Operations Research, Engineering, or a related quantitative field.
• 0–3 years of experience in applied data science / machine learning, including internships, co-ops, research, or early-career industry experience; strong academic project work may also be considered.
• Strong Python and SQL, with solid software engineering fundamentals such as Git-based workflows, code reviews, unit and integration testing, logging, readable code structure, debugging, and basic performance tuning.
• Hands-on experience with modern DS/ML tooling such as scikit-learn, PyTorch/TensorFlow, Spark/Databricks or similar, including data preparation, feature engineering, and model development.
• Demonstrated ability to turn a problem into a structured technical approach, including clear thinking around inputs, outputs, assumptions, failure modes, and evaluation.
• Exposure to building or evaluating GenAI solutions, including at least one of: RAG, structured summarization/extraction, LLM-based classification, tool/function calling, or multi-step workflows.
• Strong evaluation mindset across ML and GenAI, including metric selection, holdout testing, error analysis, scenario coverage, edge-case thinking, and basic regression testing approaches.
• Understanding of production-oriented development, including packaging code, working with APIs or services, handling configuration, monitoring outputs, and designing for maintainability.
• Strong communication skills, with the ability to explain technical outputs, limitations, and design choices in plain language.

Preferred Qualifications:

• Master’s or PhD in Computer Science, Statistics, Machine Learning, Applied Mathematics, Operations Research, Engineering, or a related quantitative field.
• 0–3 years of experience in applied data science / machine learning, including internships, co-ops, research, or early-career industry experience; strong academic project work may also be considered.
• Strong Python and SQL, with solid software engineering fundamentals such as Git-based workflows, code reviews, unit and integration testing, logging, readable code structure, debugging, and basic performance tuning.
• Hands-on experience with modern DS/ML tooling such as scikit-learn, PyTorch/TensorFlow, Spark/Databricks or similar, including data preparation, feature engineering, and model development.
• Demonstrated ability to turn a problem into a structured technical approach, including clear thinking around inputs, outputs, assumptions, failure modes, and evaluation.
• Exposure to building or evaluating GenAI solutions, including at least one of: RAG, structured summarization/extraction, LLM-based classification, tool/function calling, or multi-step workflows.
• Strong evaluation mindset across ML and GenAI, including metric selection, holdout testing, error analysis, scenario coverage, edge-case thinking, and basic regression testing approaches.
• Understanding of production-oriented development, including packaging code, working with APIs or services, handling configuration, monitoring outputs, and designing for maintainability.
• Strong communication skills, with the ability to explain technical outputs, limitations, and design choices in plain language.

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

Le poste annoncé correspond à une vacance existante.

À propos de Manuvie et de John Hancock

La Société Financière Manuvie est un chef de file mondial des services financiers qui aide les gens à prendre leurs décisions plus facilement et à vivre mieux. Pour en apprendre plus à notre sujet, rendez vous à l’adresse www.manuvie.com.

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