Manulife is making a significant investment in Advanced Analytics and GenAI to transform how Finance, Treasury, and Actuarial teams make decisions. The Associate Applied AI Engineer will help deliver AI and GenAI capabilities that integrate effectively into real business workflows by translating business challenges into structured solutions and collaborating closely with various teams.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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
Skills
- 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
- Graduate-level training with applied research or project experience in AI/ML, demonstrated through thesis work, capstone projects, publications, internships, co-ops, open-source contributions, or industry collaboration
- Hands-on GenAI experience across multiple patterns such as RAG, prompt orchestration, structured outputs, tool/function calling, and agentic workflows
- Familiarity with GenAI system components such as vector databases, embeddings, semantic search, reranking, orchestration frameworks, and prompt/version management
- Experience with cloud-based data and ML environments such as Azure, Databricks, MLflow, model registries, CI/CD pipelines, and API deployment patterns
- Experience building software beyond notebooks, including libraries, services, reusable modules, or internal tools used by others
- Familiarity with evaluation frameworks for GenAI, including test sets, rubric-based review, regression packs, and quality monitoring
- Exposure to Finance, Treasury, Insurance, IFRS-17, or Actuarial use cases, and/or familiarity with model governance practices such as documentation, validation evidence, and monitoring plans
- Experience implementing practical GenAI guardrails, such as hallucination reduction, grounding strategies, safe output formatting, access controls, and human review workflows
- Evidence of technical initiative through publications, open-source contributions, hackathons, strong capstone projects, or applied research with real-world impact
Benefits
- Health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans
- Various retirement savings plans (including pension and a global share ownership plan with employer matching contributions)
- Financial education and counseling resources
- Generous paid time off program in Canada includes holidays, vacation, personal, and sick days
- Full range of statutory leaves of absence
Company Overview
Manulife is a leading international financial services group that helps people make their decisions easier and lives better. It was founded in 1887, and is headquartered in Toronto, Ontario, CAN, with a workforce of 10001+ employees. Its website is http://www.manulife.com/.