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About This Role
Requisition ID: 270798
Salary Range: \-
*Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.*
Join a purpose driven winning team, committed to results, in an inclusive and high\-performing culture.
Global Banking and Markets
Global Banking and Markets (GBM) is a leading Canadian Capital Markets and Investment Banking business with a growing platform in the US and Latin America, operating globally for over 100 years. Scotiabank’s strong U.S. presence provides our clients an important bridge to this key global market for trade and investment flows across the Americas and the world.
Global Banking \& Markets provides a full range of investment banking, credit and risk management products and services relevant to the financing and strategic development needs of our clients. Our products include debt and equity financing, mergers \& acquisitions, corporate banking, institutional equity sales, trading and research, fixed income products, derivatives, energy, foreign exchange and precious \& metals. We also cross\-sell the full range of wholesale products and services offered by the Scotiabank Group.
Be part of an innovative, Global Capital Markets and Investment Banking business with a unique geographic footprint that puts capital to work for our clients across industries! We work together to drive ambition for every future!
Purpose
The Senior Manager, AI Platform, is an experienced engineering leader responsible for managing the delivery, adoption, and continuous improvement of enterprise AI platform capabilities that enable safe, governed, and reusable AI solutions across the organization. This role will lead platform execution for services that accelerate AI adoption, improve developer productivity, and ensure AI solutions are deployed with the reliability, security, observability, and controls required in a highly regulated environment.
You will partner with technology, data, risk, security, architecture, product, and business stakeholders to translate the AI platform roadmap into delivery plans, engineering priorities, and reusable platform capabilities including model enablement, agentic AI services, orchestration, evaluation, monitoring, guardrails, prompt and context management, integration patterns, and responsible AI controls.
What You'll Do
AI Platform Strategy \& Engineering Execution:
- Contribute to and execute the enterprise AI platform roadmap, delivery plan, and engineering priorities aligned to business needs, technology standards, and responsible AI requirements.
- Build reusable platform capabilities that enable teams to develop, test, deploy, and operate AI solutions consistently and securely across the enterprise.
- Establish scalable frameworks for:
o Model, foundation model, and large language model enablement
o Agentic AI orchestration, workflow automation, and tool integration
o Prompt, context, retrieval, and knowledge grounding services
o Reusable APIs, SDKs, templates, and reference patterns for AI engineering teams
- Implement enterprise\-grade AI platform controls including:
o Secure access, identity, entitlement, and policy enforcement for AI services
o Responsible AI guardrails, safety patterns, evaluation gates, and human\-in\-the\-loop controls
o Auditability, traceability, model usage tracking, and evidence generation
- Manage and coach platform engineering teams, setting clear delivery expectations, technical standards, sprint priorities, and operating rhythms.
- Partner with application, data, cloud, cyber, risk, and architecture teams to implement AI platform capabilities within enterprise delivery workflows.
- Ensure the AI platform supports regulated use cases by design, with controls integrated into engineering pipelines rather than applied as after\-the\-fact reviews.
AI Operations, Observability \& Trust:
- Implement and operate an AI operations framework that enables reliable, measurable, and governed AI services in production.
- Deliver platform capabilities for:
o Model and agent monitoring, performance tracking, and drift detection
o Evaluation, red\-teaming support, quality scoring, and regression testing
o Cost, token, capacity, and usage observability across AI workloads
o Incident management, rollback patterns, and continuous improvement of AI services
- Embed testing, monitoring, and governance checks into AI delivery pipelines to ensure trust, resiliency, and operational readiness by design.
AI Enablement, Reuse \& Adoption:
- Create a platform experience that makes AI capabilities easy to discover, consume, and reuse across engineering and business teams.
- Enable governed reuse through:
o AI service catalogs, reusable components, and approved reference architectures
o Standard onboarding patterns, developer documentation, and self\-service capabilities
o Reusable evaluation datasets, prompt libraries, and implementation blueprints
- Drive adoption of AI platform capabilities by working with product, engineering, architecture, and business stakeholders to turn high\-value AI use cases into reusable implementation patterns.
What You'll Bring
Required Qualifications
- Bachelor’s degree in computer science, engineering, information technology, data science, or a related technical discipline.
- Experience in financial services or other highly regulated industries, with a strong understanding of security, risk, compliance, and operational control expectations.
- 8\+ years of technology and engineering experience, including 3\+ years managing or leading platform, AI, data, cloud, or enterprise engineering teams.
- Hands\-on leadership experience with:
o AI, machine learning, generative AI, or agentic AI platforms
o Cloud\-native platform engineering, APIs, microservices, CI/CD, and infrastructure automation
o Model deployment, orchestration, monitoring, evaluation, and operational support patterns
o Strong understanding of responsible AI, AI governance, model risk, security, privacy, and regulatory expectations for production AI systems.
o Experience designing platforms that support reusable AI services, developer enablement, observability, and enterprise adoption at scale.
o Cloud platform expertise, with Azure preferred.
- Strong expertise in platform engineering practices, AI delivery lifecycle, software engineering excellence, and operating production\-grade services.
- Strong understanding of:
o AI security, privacy, responsible AI, model lifecycle management, and regulatory compliance in a financial services environment
- Proven ability to work directly with engineers, architects, product leaders, data scientists, risk partners, and senior stakeholders to deliver platform outcomes.
- Strong communication skills with the ability to translate AI platform strategy into clear engineering priorities, delivery plans, stakeholder updates, and measurable outcomes.
Interested?
If your experience is closely related but doesn’t align perfectly with every qualification, we do encourage you to apply \- you might be the right candidate for this or other roles at Scotiabank!
At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That’s why we work to grow and diversify talent and engage employees in a performance\-oriented culture.
What's in it for you?
Scotiabank wants you to be able to bring your best self to work – and life, every day. With a focus on holistic well\-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.
\#Dallas
Location(s): United States : Texas : Dallas
Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.
At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.
Scotiabank is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Scotiabank, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Scotiabank AI Hiring
Scotiabank has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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