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About This Role
Who We Are
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Welcome to TELUS Digital — where innovation drives impact at a global scale. As an award\-winning digital product consultancy and the digital division of TELUS, one of Canada’s largest telecommunications providers, we design and deliver transformative customer experiences through cutting\-edge technology, agile thinking, and a people\-first culture.
With a global team across North America, South America, Central America, Europe, and APAC, we offer end\-to\-end expertise across eight core service areas: Digital Product Consulting, Digital Marketing Services, Data \& AI, Strategy Consulting, Business Operations Modernization, Enterprise Applications, Cloud Engineering, and QA \& Test Engineering.
From mobile apps and websites to voice UI, chatbots, AI, customer service, and in\-store solutions, TELUS Digital enables seamless, trusted, and digitally powered experiences that meet customers wherever they are — all backed by the secure infrastructure and scale of our multi\-billion\-dollar parent company.
Location and Flexibility
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Our Associate Principal, AI \& CX Solutions Strategist, is an integral part of our Strategy team at TELUS Digital. This role will have the option to be in a Work From Near (Hybrid) capacity based out of our Vancouver, BC office, OR in a Work From Anywhere (Remote) capacity from within Canada.
The Opportunity
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We are seeking an agile, forward\-thinking Associate Principal, AI \& CX Solutions Strategist to drive digital and AI transformation initiatives across our global contact center operations.
Note: *While this is a leadership\-level role in our consulting practice, this is not a legacy systems role. We are explicitly looking for a hybrid strategist and technologist (ideally with 3–6 years of highly relevant experience) who understands the modern AI ecosystem (Conversational AI, LLMs, AI Agents) and can translate those capabilities into high\-impact business outcomes.*
Operating at the intersection of management consulting and solutions architecture, you will partner closely with CX strategy and AI product teams to move clients rapidly from concept to working proofs\-of\-concept. The ideal candidate has a "builder" mindset, a deep understanding of the modern tech stack, and the ability to confidently counsel enterprise clients on adopting cutting\-edge CX innovations to redefine the future of customer experience.
Responsibilities
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- Bridge Strategy \& Technology: Act as a strategic bridge between business goals and AI capabilities, defining UX features, AI agent\-assist tools, and the success metrics for modern deployments.
- Strategic Prototyping: Move clients beyond theoretical frameworks by designing AI\-driven CX strategies and advising on pilot programs and implementation readiness.
- Build Business Cases: Partner with Product, CCaaS, and Data \& AI teams to design high\-level architecture visions and build functional, strategic business cases for both modern AI tools and foundational contact center tech.
- Drive Value: Identify, quantify, and prioritize tech/AI opportunities to radically enhance the customer experience, improve operational agility, and drive ROI.
- Shape the Roadmap: Provide continuous, hands\-on input into the CX Product roadmap, feeding new, cutting\-edge product features back into CX strategy engagements.
- Enable the Organization: Translate complex tech/AI architecture visions into actionable deployment plans and collaborate with cross\-functional teams to keep go\-to\-market and sales collateral relevant.
- Champion Innovation: Stay relentlessly up\-to\-date on emerging tech/AI trends in the contact center space (e.g., autonomous AI agents, LLM advancements), inspiring clients and internal teams with the "art of the possible."
Skills \& Qualifications
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- Experience: 5\-10 years of experience in management consulting, digital/product strategy, or a pre\-sales/solutions engineering role, ideally within a high\-growth tech or AI\-forward environment.
- Tech Stack Fluency: Proficient in modern contact center ecosystems as an operator and/or implementation lead. Experience with foundational CCaaS (Zendesk, Amazon Connect, Twilio, Intercom, or NiCE) is preferred, coupled with a strong understanding of emerging Conversational AI, GenAI agents, and LLM integrations.
- The "Builder" Mindset: You thrive in ambiguity, move with velocity, and possess the agility to translate highly technical AI concepts into clear, actionable business strategies for non\-technical stakeholders.
- Analytical Rigor: Strong strategic and analytical skills, with a proven ability to quantify the business value and ROI of tech/AI solutions.
- Executive Communication: Excellent verbal and written communication skills; you can effortlessly pivot from a technical architecture discussion to an executive board presentation.
- Curiosity: Deep intellectual curiosity and a genuine passion for the ‘latest and greatest’ improvements in digital CX and AI.
Equal Opportunity Employer
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*At TELUS Digital, we are proud to be an equal opportunity employer and are committed to creating a diverse and inclusive workplace. All aspects of employment, including the decision to hire and promote, are based on applicants’ qualifications, merits, competence, and performance without regard to any characteristic related to diversity.*
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Compensation Range: CA$132K \- CA$165K
Salary Context
This $132K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TELUS Digital, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($148K) sits 18% below the category median. Disclosed range: $132K to $165K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TELUS Digital AI Hiring
TELUS Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Ontario, CA, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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