Senior applied ai engineer – genai & actuarial systems

Société Financière Manuvie; Toronto; Hybrid; CAD 129,000 - 179,000; Full time

Job summary

Manulife’s AI team is scaling advanced analytics to transform decision making across Finance and actuarial functions. You will translate business problems into machine learning and GenAI use cases, build reusable production components, and ensure explainability, monitoring, and governance across enterprise workflows.

You will collaborate with data and ML engineering to productionize pipelines, implement robust evaluation, and mentor junior scientists while maintaining strict data privacy and

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 TensorFlow, 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.

Responsibilities

• Translate business problems into AI use cases and solutions for actuarial needs.
• Own end-to-end solution design for actuarial AI, including data flow, modeling approach, evaluation, and controls.
• Build GenAI capabilities such as retrieval-based solutions, structured summarization, and guided analytical workflows.
• Set evaluation criteria and evidence through backtesting, scenario analysis, and human-in-the-loop reviews.
• Collaborate with data/ML engineering and software teams to productionize pipelines, CI/CD, deployment, and monitoring.
• Work in a governed environment with documentation, model risk evidence, and data/privacy controls.
• Mentor junior scientists and promote reusable design templates and evaluation practices.

Skills

Python

SQL

Git workflows

Testing

scikit-learn

PyTorch/TensorFlow

Spark/Databricks

Model development

Communication

Tools

Databricks

Spark

Git

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

• 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 TensorFlow, 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

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.

Manuvie est un employeur qui souscrit au principe de l’égalité d’accès à l’emploi

Chez Manulife/John Hancock nous valorisons notre diversité. Nous nous efforçons d’attirer, de perfectionner et de maintenir une main d'oeuvre qui est aussi diversifiée que nos clients, et de favoriser la création d’un milieu de travail inclusif qui met à profit la diversité de nos employés et les compétences de chacun. Nous nous engageons à assurer un recrutement, une fidélisation, une promotion et une rémunération équitables, et nous administrons toutes nos pratiques et tous nos programmes sans discrimination en raison de la race, de l’ascendance, du lieu d’origine, de la couleur, de l’origine ethnique, de la citoyenneté, de la religion ou des croyances ou des convictions religieuses, du genre (y compris grossesse et affection liée à une grossesse), de l’orientation sexuelle, des caractéristiques génétiques, du statut d’ancien combattant, de l’identité de genre, de l’expression de genre, de l’âge, de l’état matrimonial, de la situation de famille, d’une invalidité ou de tout autre motif protégé par la loi applicable.

Nous nous sommes donné comme priorité d’éliminer les obstacles à l’accès égalitaire à l’emploi. C’est pourquoi un représentant des Ressources humaines collaborera avec les candidats qui demandent accommodement raisonnable pendant le recrutement. Tous les renseignements communiqués pendant le processus de demande d'accommodement seront stockés et utilisés conformément aux lois et aux politiques applicables de Manuvie. Pour demander une mesure d’accommodement raisonnable dans le cadre du recrutement, écrivez à [e-mail].

Région de référence du salaire

Toronto, Ontario

Modalités de travail

Hybride

L’échelle salariale devrait se situer entre

$129,400.00 CAD - $179,400.00 CAD

Les employés ont également la possibilité de participer à des programmes incitatifs et de recevoir une rémunération liée à la performance de l’entreprise et des individus. Le salaire réel variera selon les conditions du marché local, la région géographique et les facteurs propres au poste, tels que les connaissances, les compétences, les qualifications, l’expérience et la formation. Si vous postulez à ce poste en dehors de votre lieu principal, veuillez contacter [e-mail] pour connaître la fourchette salariale applicable à votre région.

Manuvie offre aux employés admissibles une vaste gamme d’avantages sociaux personnalisables, notamment une assurance soins médicaux, soins dentaires, santé mentale, soins de la vue, invalidité de courte et de longue durée, assurance vie et assurance DMA, assurance adoption, de maternité de substitution et de soins médicaux non urgents ainsi que des programmes d’aide aux employés et leur famille. Nous proposons également aux employés admissibles différents régimes d’épargne-retraite (y compris des régimes de rente et un programme international d’actionnariat assortie de cotisations patronales de contrepartie) ainsi que des ressources en matière d’éducation et de conseils financiers. Notre généreux programme de congés rémunérés au Canada comprend les jours fériés, les congés annuels, les congés personnels et les congés de maladie, et nous offrons toute la gamme des congés autorisés prévus par la loi. Si vous posez votre candidature à ce poste aux États-Unis, veuillez écrire à [e-mail] pour obtenir de plus amples renseignements sur les dispositions relatives aux congés rémunérés spécifiques aux États-Unis.

Nous utilisons des technologies de données et d’analytique, telles que l’intelligence artificielle (IA), ainsi que des outils de traitement automatisé pour analyser et traiter les renseignements que vous nous fournissez ou que des tiers nous transmettent dans le cadre du processus de demande. Pour en savoir plus, veuillez consulter notre énoncé sur la collecte de renseignements personnels.