Interested in this AI/ML Engineer role at BMO Financial Group?
Apply Now →Skills & Technologies
About This Role
Application Deadline:
11/30/2026 Address:
320 S Canal Street Job Family Group:
Data Analytics & Reporting The Team - We accelerate BMO’s AI journey by building cloud-native AI solutions. Our team combines engineering excellence with cutting-edge AI to deliver scalable, secure, and responsible solutions that power business innovation across the bank. We enable and accelerate our partners on their AI journeys across the enterprise, helping teams across BMO unlock value at scale. We support one another in times of need and take pride in our work. We are engineers, AI practitioners, platform builders, thought leaders, multipliers, and coders. Above all, we are a global team of diverse individuals who enjoy working together to create smart, secure, and scalable solutions that make an impact across the enterprise. Our ambition is bold: deploy our capital and resources to their highest and most profitable use through a digital-first operating model, powered by data and AI-driven decisions.
The Impact
As a Principal AI & Cloud Engineer , you are a hands-on technical developer who designs, builds, and scales cloud-native AI solutions and products. You help set engineering standards, establish patterns, mentor senior engineers, and partner with multiple teams to deliver resilient, governed, and cost-efficient AI at enterprise scale. You’ll help shape and evolve our AI cloud strategy from model serving and LLMOps to security, observability, and compliance so teams across the bank can innovate safely and rapidly.
You will advance BMO’s Digital First strategy by:
- Defining reference and production-grade solutions for AI/GenAI on cloud (Azure/AWS preferred; multi-cloud aware).
- Building reusable, secure, and observable components (APIs, SDKs, microservices, pipelines).
- Operationalizing LLMs and RAG with strong controls and Responsible AI guardrails.
Driving platform roadmaps that enable faster delivery, lower risk, and measurable business outcomes. *
What’s In It for You
- Influence the technical direction of AI and the platform primitives others build on.
- Ship high-impact systems used across many business lines and products.
- Work across the full stack: cloud infra, data/feature pipelines, model serving, LLMOps, and DevSecOps.
Partner with a leadership team invested in your growth and thought leadership. *
Responsibilities Infrastructure & Platform Builder
- Design, build, and operate cloud-native AI infrastructure for ML/GenAI workloads:
- Compute: GPU/CPU clusters, autoscaling, spot instance strategies
- Networking: Azure VNet, Private Link, peering, multi-region HA/DR
- Storage & Databases: high-performance data lakes (e.g., Azure Data Lake Storage) , relational DBs, vector DBs (FAISS, Milvus, Pinecone, pgvector)
- Security: IAM, Key Vault-backed secrets management, encryption, policy-as-code
- Implement observability and reliability for AI infra:
- Metrics (latency, throughput, GPU utilization, cost)
- Logging/tracing (OpenTelemetry), SLOs/SLIs for infra services
- Build CI/CD and GitOps pipelines for infrastructure-as-code (Terraform/Bicep) and AI platform components
Drive FinOps for AI infra: GPU rightsizing, caching, inference optimization, cost governance *
Application & Service Enablement
- Enable frontend and backend services for AI platforms:
- Secure APIs, microservices, and event-driven architectures
- Integration with custom model runtimes (TensorRT-LLM, vLLM, Triton/KServe)
- Provide infrastructure support for RAG systems: embeddings, chunking, retrieval pipelines
Ensure scalable serving infrastructure for LLMs and ML models with caching and token optimization *
Strategy & Architecture
- Define and evolve AI infrastructure reference architecture for cloud (Azure preferred):
- Container orchestration (Kubernetes), service mesh, ingress
- Serverless/event-driven patterns for AI pipelines
- Multi-region, HA/DR, compliance-ready designs
Establish standards and best practices for containerization, IaC, and secure networking for AI systems *
Security, Risk & Governance
- Implement defense-in-depth for AI infra:
- IAM least privilege, private networking, KMS/Key Vault, SBOM, image signing
- Ensure compliance and Responsible AI controls at infra level:
Data residency, encryption, lineage, audit readiness *
Delivery & Operations
- Lead infrastructure discovery and solution design with stakeholders
- Operate platforms with SRE principles: error budgets, incident response, chaos testing
Mentor engineers; create reusable IaC modules, templates, and golden paths *
Must-Have Qualifications
- Bachelor’s/Master’s/PhD in CS, Engineering, or related field
- 7+ years building large-scale distributed cloud infrastructure
- 5+ years hands-on with Azure/AWS
- Proven experience with AI/ML infra: GPU clusters, Kubernetes, CI/CD, observability
- Strong in IaC (Terraform/Bicep), Kubernetes, networking, security
- Expertise in cloud-native patterns: containers, service mesh, serverless
- Familiarity with MLOps/LLMOps infra: model serving, feature stores, vector DBs
- Programming in Python (infra automation) and one of Go/TypeScript for tooling
- Understanding of frontend/backend integration for AI services
- Familiarity with MLOps/LLMOps infra: model serving, feature stores, vector DBs
- Programming in Python (infra automation) and one of Go/TypeScript for tooling
Understanding of frontend/backend integration for AI services *
Nice-to-Have
- GPU optimization (CUDA/NCCL, TensorRT-LLM)
- Observability tools (Prometheus, Grafana, OpenTelemetry)
- Event streaming (Kafka/Azure Event Hubs), real-time systems
Experience with AI platform products (Azure ML, MLflow, KServe, Hugging Face) *
Success Metrics
- Reliability & Performance: SLOs met for infra services, GPU utilization optimized
- Security & Compliance: Zero critical findings, auditable infra
- Cost Efficiency: Reduced GPU/infra spend via FinOps strategies
- Developer Velocity: Faster provisioning and deployment of AI infra
- Technical Leadership: Influence on infra standards, mentorship, reusable patterns
Salary :
$120,000.00 - $250,000.00 Pay Type:
Salaried The above represents BMO Financial Group’s pay range and type.
Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.
BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards
About Us
At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.
As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.
To find out more visit us at http://jobs.bmo.com/us/en
BMO is proud to be an equal employment opportunity employer. We evaluate applicants without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other legally protected characteristics. We also consider applicants with criminal histories, consistent with applicable federal, state and local law.
BMO is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e-mail to [email protected] and let us know the nature of your request and your contact information.
Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.
Salary Context
This $120K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $120K to $250K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
BMO Financial Group AI Hiring
BMO Financial Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $250K - $250K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.