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About This Role
Requisition ID: 269886
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
As an AI Enablement Engineer, you will act as the technical enablement and functional interface between enterprise Artificial Intelligence platforms and the organization. You will be responsible for enabling scalable adoption, effective usage, and measurable value realization of generative AI, LLM\-powered services, and intelligent productivity platforms across the enterprise.
This role is highly technical in orientation but not development focused. Your mandate is to ensure that AI platforms are understood, operationalized, and embedded into business workflows through structured enablement, technical documentation, and functional guidance. You will translate platform capabilities into practical usage patterns, ensuring teams can safely and effectively leverage AI tools as they mature.
What You'll Do
Platform Enablement \& User Onboarding
- Design and execute technical onboarding frameworks for enterprise AI platforms, including capability overviews, access models, guardrails, and supported use patterns.
- Produce technical enablement artifacts such as system usage guides, configuration references, operating manuals, and structured platform documentation.
- Partner with technology, architecture, data, and business teams to integrate AI capabilities into existing workflows and operating models.
- Analyze end\-to\-end workflows and identify opportunities where AI\-driven automation or augmentation can improve efficiency and consistency.
- Lead structured discovery and intake sessions to capture technical and business requirements for AI\-enabled workflows.
Capability Roadmap \& Platform Strategy
- Track and assess the evolving capabilities of enterprise AI platforms, including generative AI services, copilots, and intelligent productivity tools.
- Translate vendor and platform roadmaps into internal capability roadmaps, clearly outlining what is available, what is coming, and how it should be used.
- Evaluate new features for technical readiness, risk alignment, and business applicability, and communicate implications to stakeholders.
- Act as a functional advisor to platform and engineering teams during feature rollouts and upgrades.
Technical Training \& Knowledge Enablement
- Build and maintain best\-practice repositories, including prompt engineering patterns, usage standards, workflow templates, and reference examples.
- Establish and manage a distributed AI Champions network to support advanced usage, experimentation, and peer enablement.
- Support platform delivery teams by ensuring users are technically prepared to adopt new capabilities through guided usage and structured learning paths.
- Enable smooth transition from platform implementation to steady\-state usage through targeted enablement and adoption support.
Functional Support \& Continuous Improvement
- Serve as the primary functional and enablement point of contact for AI platform usage questions, workflow troubleshooting, and best\-practice guidance.
- Triage functional issues, usability gaps, and platform defects, escalating appropriately to engineering, architecture, or IT teams.
- Collect, synthesize, and prioritize user feedback to inform future platform enhancements, roadmap decisions, and enablement strategies.
- Perform functional validation and user acceptance testing to ensure AI solutions meet business and usability requirements.
- Continuously refine enablement materials and workflows based on real\-world usage and post\-rollout feedback.
Value Realization, Adoption \& Metrics
- Define and monitor AI adoption and usage metrics, including activation, engagement, satisfaction, and feature utilization.
- Measure productivity impact, efficiency gains, and value realization through surveys, interviews, and usage data.
- Document and communicate internal success stories, proven patterns, and repeatable use cases to drive broader adoption.
- Produce enablement and adoption reporting that demonstrates business impact and informs leadership decision\-making.
What You'll Bring
- Demonstrated experience in technical enablement, platform adoption, systems analysis, or digital transformation roles.
- Experience working in Azure, Databricks and Google native services and environments
- Strong conceptual understanding of Generative AI, Large Language Models (LLMs), AI copilots, and enterprise productivity platforms.
- Ability to interpret complex platform capabilities and translate them into clear operational guidance and usage standards.
- Excellent written and verbal communication skills, particularly for technical documentation and stakeholder presentations.
- Strong user\-centric mindset with the ability to anticipate adoption challenges and design practical solutions.
- Experience defining metrics, analyzing adoption data, and producing insights reports.
- 5\-7 years of experience in a role such as Technical Systems Analyst, Platform Enablement Lead, or Technical Product Owner
- Bachelor’s degree in, Information Sciences, Computer Information Systems, or a related field.
- Spanish fluency (nice to have).
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 \|\| United States : Texas : Austin \|\| United States : Texas : Houston
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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