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About This Role
Date limite pour présenter sa candidature :
10/30/2026 Adresse :
2623 Camino Ramon Groupe de famille d'emploi :
Solutions clientèle About the Opportunity:
Imagine helping build the next generation of AI-powered products that transform how Commercial Banking teams work, serve clients, and make decisions.
As a Product Owner within BMO's AI & Analytics team, you will operate at the intersection of business, technology, data, and user experience, turning complex challenges and strategic opportunities into solutions that deliver measurable business value.
You will partner with business leaders, relationship managers, sales professionals, data scientists, engineers, and product partners to identify opportunities, shape product direction, and drive AI-enabled capabilities from concept through delivery, adoption, measurement, and continuous improvement.
This role is ideal for someone who thrives in ambiguity, takes ownership, enjoys solving complex business problems, and is energized by turning vision into outcomes that create meaningful impact across Commercial Banking.
Product Owner, AI & Analytics
What if your next role wasn't about managing a backlog?
What if it was about taking complex business challenges and transforming them into AI-powered products that change how bankers work, how clients are served, and how decisions are made?
As a Product Owner within BMO's AI & Analytics team, you will help shape and deliver innovative products that leverage analytics, artificial intelligence, machine learning, and data-driven insights to solve meaningful challenges across Commercial Banking.
You will work directly with business leaders, relationship managers, sales professionals, data scientists, engineers, and product partners to identify opportunities, shape product strategy, and transform ideas into products that drive measurable business value.
This role is ideal for someone who enjoys shaping product vision, solving complex business problems, and leading products from concept through adoption and business impact.
What You'll Do
Turn Ideas Into Products
Take customer needs, business challenges, and strategic opportunities and transform them into products that create measurable business value.
You will help answer questions such as:
- What problem are we solving?
- Why does it matter?
- Who are we solving it for?
- What experience will create the greatest value?
- What should we prioritize first?
- How do we build something users will adopt and trust?
Starting with customer needs, business challenges, and strategic opportunities, you will help develop product strategies, priorities, and roadmaps that deliver measurable business outcomes.
Lead Discovery and Shape Product Direction
- Partner with business stakeholders to understand challenges, opportunities, workflows, priorities, and user needs.
- Facilitate discovery discussions that align stakeholders, challenge assumptions, and uncover opportunities for innovation.
- Gather and synthesize customer, business, operational, and data-driven insights to identify opportunities, evaluate alternatives, and inform product direction.
- Examine processes and workflows to identify opportunities for simplification, optimization, and improved business performance.
- Help stakeholders define, prioritize, and validate opportunities that create value.
Challenge assumptions and ask thoughtful questions that help teams focus on the problems that matter most. *
Apply Business Acumen to Drive Better Decisions
- Develop a strong understanding of Commercial Banking objectives, customer needs, operational processes, and strategic priorities.
- Evaluate opportunities through both a product and business lens, balancing customer value, commercial impact, risk, effort, and long-term benefit.
- Build credibility with business leaders by connecting product decisions to measurable outcomes.
Help stakeholders evaluate trade-offs and investment decisions that maximize value. *
Bridge Business and Technology
Act as the link between business stakeholders and delivery teams, translating business goals, user workflows, data requirements, and technology capabilities into clear product requirements.
Translate business needs into:
- Product roadmaps
- Product requirements
- User stories
- Acceptance criteria
- Prioritized backlogs
Ensure teams understand what needs to be built, why it matters, and how success will be measured.
Champion the User Experience
Advocate for end users throughout the product lifecycle.
Continuously evaluate how products can become:
- Easier to use
- More valuable
- More intuitive
- Better aligned to user workflows
Focus not simply on feature delivery, but on adoption, customer outcomes, and long-term value creation.
Drive AI Product Delivery
Work closely with:
- Data Scientists
- Data Engineers
- Software Developers
- Architects
- Scrum Masters
- Product Managers
- Business Stakeholders
to bring AI-powered capabilities from discovery through implementation, adoption, and continuous improvement.
- Balance customer value, business priorities, technical feasibility, and delivery timelines.
- Execute product priorities through established delivery processes and cross-functional team practices while maintaining focus on strategy and outcomes.
Partner with delivery teams to ensure solutions align with business objectives and user needs. *
Influence Without Authority
- Build strong relationships across stakeholder groups.
- Navigate competing priorities and align diverse perspectives to move decisions forward.
- Communicate effectively across business and technical audiences.
- Help teams make informed decisions that maximize business outcomes.
Success in this role requires strong communication skills, business credibility, intellectual curiosity, and the ability to unite teams around a common product vision.
What Makes Someone Successful In This Role
Ownership and Initiative
- You are proactive.
- You do not wait for perfect information.
- You take accountability for outcomes and consistently move work forward.
- You demonstrate resilience, adaptability, and a growth mindset.
Strong Critical Thinking
- You enjoy solving difficult problems.
- You can take abstract concepts and transform them into a clear product vision, structured requirements, and executable plans.
- You ask insightful questions and quickly identify what matters most.
- You use data, customer insights, research, and sound judgment to evaluate options and make informed decisions.
Product Mindset
- You think beyond requirements.
- You think about customers, adoption, outcomes, workflows, and long-term value creation.
- You connect customer needs, business objectives, data insights, and technology capabilities into cohesive product strategies.
Business Acumen
- You understand how organizations create value and how products contribute to business results.
- You can engage credibly with stakeholders and connect product decisions to strategic priorities, customer outcomes, operational improvements, and financial performance.
- Experience in financial services, banking, analytics, payments, lending, treasury, consulting, or related industries is highly valued.
Product Delivery Experience
- You have experience translating product strategy into execution through planning, prioritization, stakeholder alignment, and cross-functional collaboration.
- You understand product delivery practices, backlog management, release planning, and product development lifecycles.
- You know how to help teams execute effectively while maintaining focus on broader product goals and business outcomes.
Curiosity and Continuous Learning
- You enjoy learning new domains, technologies, and business concepts.
- You seek feedback, challenge assumptions, and continuously improve.
You remain curious about emerging technologies, AI capabilities, customer needs, and business opportunities. *
Qualifications
What You’ll Bring
- Typically 5-7 years of relevant experience and post-secondary education in a related discipline, or an equivalent combination of education and experience.
- Experience in Product Ownership, Product Management, Business Analysis, Consulting, Strategy, or related disciplines
- Experience translating business requirements into product requirements and user stories
- Experience working with cross-functional delivery teams
- Experience in financial services, banking, analytics, data products, digital products, or AI-enabled solutions
- Experience supporting Agile delivery teams
Nice To Have
- AI, machine learning, or advanced analytics experience
- Jira, Rovo, Confluence, Azure DevOps, or similar tooling experience
- Agile certifications or Product Owner certifications
- Commercial Banking or Treasury & Payments experience
Why This Role Is Different
This is not a role focused solely on requirements gathering or backlog administration.
This is an opportunity to help define and build the next generation of AI-powered capabilities that will transform how Commercial Banking teams engage clients, generate insights, make decisions, and create value.
If you enjoy taking ideas from whiteboard discussions to real-world products used by thousands of users, we'd love to hear from you.
Salaire :
$90,000.00 - $180,000.00 Type de rémunération :
Salaire Ce qui précède représente la fourchette et le type de rémunération de BMO Groupe financier.
Les salaires varieront en fonction de facteurs comme l’emplacement, les compétences, l’expérience, les études et les qualifications pour le poste et pourront inclure une structure de commissions. Les salaires pour les postes à temps partiel seront calculés au prorata du nombre d’heures travaillées régulièrement. Pour les rôles à commission, le salaire susmentionné représente la cible de BMO Groupe financier pour la première année au poste.
La rémunération totale offerte par BMO variera selon le type de rémunération associé au poste et peut comprendre des primes de rendement, des primes discrétionnaires ainsi que d’autres avantages et récompenses. BMO offre également une assurance santé, le remboursement des frais de scolarité, une assurance accident et une assurance vie, ainsi que des régimes d’épargne-retraite. Pour en savoir plus sur nos avantages sociaux, consultez le site : https://jobs.bmo.com/ca/fr/R%C3%A9mun%C3%A9ration-globale
À propos de nous
À BMO, nous sommes animés par une raison d’être commune : Avoir le cran de faire une différence dans la vie, comme en affaires. Cette raison d’être nous invite à entraîner des changements positifs et durables pour nos clients, nos collectivités et nos employés. En travaillant ensemble, en innovant et en repoussant les limites, nous transformons des vies et des entreprises et favorisons la croissance économique partout dans le monde.
En tant que membre de l’équipe de BMO, vous êtes valorisé, respecté et entendu, et vous avez plus de moyens pour progresser et obtenir des résultats. Nous nous efforçons de vous aider à obtenir des résultats dès le premier jour, pour vous-même et nos clients. Nous vous offrirons les outils et les ressources dont vous avez besoin pour franchir de nouvelles étapes, car vous aidez nos clients à franchir les leurs. Au moyen d’une formation et d’un coaching approfondis, de pair avec le soutien de la direction et des occasions de réseautage, nous vous aiderons à acquérir une expérience enrichissante et à élargir votre groupe de compétences.
Pour en savoir plus, visitez-nous à l’adresse http://jobs.bmo.com/us/en
BMO est fier d’être un employeur qui favorise l’égalité d’accès à l’emploi. Nous évaluons les demandeurs sans tenir compte de la race, de la religion, de la couleur, de l’origine nationale, du sexe (y compris la grossesse, les naissances ou les problèmes de santé connexes), de l’orientation sexuelle, de l’identité de genre, de l’expression de genre, du statut de transgenre, des stéréotypes sexuels, de l’âge, du statut d’ancien combattant protégé, du statut de personne handicapée ou de toute autre caractéristique légalement protégée. Nous tenons également compte des demandeurs qui ont des antécédents criminels, conformément aux lois fédérales, étatiques et locales applicables.
BMO s’engage à travailler avec les personnes handicapées et à leur offrir des mesures d’adaptation raisonnables. Si vous avez besoin d’une mesure d’adaptation raisonnable en raison d’une invalidité dans le cadre d’une partie du processus d’emploi, veuillez envoyer un courriel à l’adresse [email protected] et nous informer de la nature de votre demande et de vos coordonnées.
Remarque aux recruteurs : BMO n’accepte pas les curriculum vitæ non sollicités provenant de toute source autre que le candidat directement. Tout curriculum vitæ non sollicité envoyé à BMO, directement ou indirectement, sera considéré comme la propriété de BMO. BMO ne paiera aucuns frais pour les placements découlant de la réception d’un curriculum vitæ non sollicité. Une agence de recrutement doit d’abord détenir une entente de service écrite valide et dûment signée avant d’envoyer des curriculum vitæ.
Salary Context
This $90K-$180K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BMO Financial Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $90K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
BMO Financial Group AI Hiring
BMO Financial Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Ramon, CA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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