Interested in this AI/ML Engineer role at TELUS Digital?
Apply Now →Skills & Technologies
About This Role
Director, CX AI Delivery
Who We Are
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, Africa, 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 & Flexibility
This role will operate remotely in the United States or Canada OR be based out of one of our major North American office locations - Charlottesville, VA, Durham, NC, Columbus, OH, Boston, MA, Toronto, ON, or Vancouver, BC.
This role may involve travel of up to 25% to contact center and client locations.
The Opportunity
We are seeking a Director, CX AI Delivery: a consultant and delivery expert who can turn complex contact center and AI programs into predictable, measurable outcomes. You will lead delivery across our CX AI portfolio, spanning Contact Center as a Service (CCaaS) platforms, conversational and agentic AI, agent assist, and CX intelligence, and serve as a trusted advisor to client executives. You will manage a portfolio of client engagements (generally ~$15–20M in revenue), directly lead delivery, product, and engineering leaders, and step in to stabilize and recover programs when needed.
This is a delivery-first leadership role with an AI lens. You will bring the rigor of a seasoned delivery executive together with a working command of how AI is changing CX: from AI-driven routing and virtual agents to copilots, knowledge, and agentic automation. You will help clients adopt these capabilities responsibly, at scale, and in iterative releases that get value into production quickly. You will also help evaluate vendor and partner solutions and decide how and when new tools enter the ecosystem.
This position reports to the Managing Director, CX AI Delivery and typically manages 8–12 direct reports, including senior managers, managers, and principal-level delivery and technical leaders. It is primarily evaluated on delivery success, client and team feedback, forecast accuracy, portfolio profitability, and growth of key accounts. This is an opportunistically billable position.
Responsibilities
Strategic Account Governance & Growth
- Be the executive partner. Act as the primary escalation point and strategic advisor for client executives, ensuring TELUS Digital is viewed as a “Doctor” (consultative partner), not a “Waiter” (order taker).
- Run portfolio health. Drive the commercial success of a ~$15–20M portfolio, leading Quarterly Business Reviews that connect delivery progress to the client’s long-term CX and business goals.
- Grow organically. Identify expansion opportunities in existing accounts by understanding client pain points and bringing a relevant point of view on CX and AI. Support renewals, upsell, and expansion in partnership with strategic sales and customer success teams.
- Extend into managed services and advisory as the practice matures: shape Day 2 support models, operational readiness, and advisory engagements that keep clients engaged after go-live.
Delivery Excellence & Risk Mitigation
- Lead a global, multidisciplinary team across multiple concurrent workstreams, ensuring product, engineering, and delivery leaders operate as a cohesive unit to deliver measurable outcomes.
- Manage risk proactively. Identify “red” and “yellow” programs early; step into recovery situations to provide clarity, reset expectations, and stabilize teams and client relationships.
- Push for iterative delivery. Favor smaller releases and faster cycles over big-bang launches, especially for AI capabilities where learning in production is essential.
- Optimize delivery models, balancing onshore/offshore teams, automation, and repeatable assets to improve margins and scalability.
- Manage vendor and partner ecosystems. Work with CCaaS and AI platform partners (e.g., Genesys, Google, AWS, Zendesk, Five9, ElevenLabs, Cresta, Parloa) to deliver joint solutions as well as surrounding services in the client ecosystem.
- Partner on pre-sales. Support the commercial team to shape innovative solutions and lead scoping, estimation, and handoffs into delivery so engagements start with clear requirements and goals.
Leadership & Talent Development
- Coach and mentor delivery, product, and engineering leaders on leading their own teams and navigating complex client dynamics.
- Build a high-performance culture with regular 1:1s and performance reviews grounded in radical candor and growth, delivering feedback upward, across, and down with grace and clarity.
- Raise the bar on talent. Participate in senior-level hiring, contribute to the Delivery Playbook, and build career paths and skill development (including AI and platform certification pipelines).
Operational & Financial Stewardship
- Own portfolio financials: gross margin, utilization, and effective billable rates.
- Forecast accurately. Maintain precise revenue and resource forecasts so the business can plan hiring and growth with confidence.
- Influence with data. Use reporting language, data visualization, and storytelling to influence internal and external decisions and inspire confidence in stakeholders.
- Collaborate across the business with peers and practice leaders to improve internal processes and operational efficiency, and bring energy to moments that matter: new relationships, key presentations, and recovery situations.
Qualifications
- 10+ years in the technology industry, including 4+ years in a senior leadership role in a professional services, consulting, or agency environment.
- Proven record managing a diverse portfolio of client accounts (ideally $10M+ in annual revenue) with a focus on retention and organic growth.
- Experience delivering large-scale CX or contact center transformations that influenced VP- and C-level decision making.
- Working knowledge of how AI is applied in CX: conversational and voice AI, agent assist and copilots, AI-driven routing, knowledge and content for AI, and CX intelligence; ability to discuss architecture, risk, and ROI credibly with both technical and executive audiences.
- Hands-on delivery experience with one or more leading CCaaS or CX AI platforms (e.g., Genesys, Amazon Connect, Zendesk, Google); platform certifications and experience across multiple verticals are a plus.
- Deep understanding of the SDLC and Agile delivery, with a track record of leading complex, custom-built software solutions from discovery to launch, and comfort with iterative, experiment-driven delivery.
- Experience managing and coaching leaders across Product, Engineering, and Delivery, and leading large, globally distributed teams.
- Strong command of professional services metrics, including gross margin, utilization, forecast accuracy, and effective billable rates.
- Exceptional ability to navigate “recovery situations” and high-stakes client conversations with poise, clarity, and a solution-oriented mindset.
- Ability to translate complex delivery data into compelling narratives for QBRs and executive stakeholders.
- Understanding of regulatory requirements in contact center operations (e.g., PCI, HIPAA, TCPA, PIPEDA) is a plus.
Equal Opportunity Employer
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.
We will only use the information you provide to process your application and to produce tracking statistics. Since we do not request personal data deemed sensitive, we ask you to abstain from sharing that information with us.
For more information on how we use your information, see our Privacy Policy.
Compensation Range: CA$150K - CA$190K
Salary Context
This $150K-$190K range is below the median 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 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $150K to $190K.
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.
TELUS Digital AI Hiring
TELUS Digital has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Ontario, CA, US, Charlottesville, VA, St. Louis, MO, US. Compensation range: $156K - $190K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.