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About This Role
Description
Senior Solutions Architect AI/ML
Work Style: Hybrid \- San Francisco Bay Area
Compensation: $150K \- $160K USD annual
We are looking for a Senior Solutions Architect to lead our technical engagements with the teams building the future of AI. In this role, you will be the technical authority and trusted partner for our most strategic customers, the pioneers in AGI/ASI development.
You will be at the forefront of AI innovation, bridging advanced research with practical engineering. Your primary focus will be on designing sophisticated data solutions that accelerate the creation of next\-generation AI. This involves providing technical thought
leadership and fostering strong collaborations with leading AI research teams globally.
The ideal candidate is a seasoned architect with deep expertise in Applied ML, a history of advising sophisticated technical customers, and a passion for solving the most complex data challenges in AI.
The Impact You'll Make
- Shape the Future of AI: Act as a strategic partner to AGI/ASI research teams, designing the foundational data solutions that will power breakthrough models.
- Drive Technical Strategy: Establish the architectural best practices and reference patterns for our most advanced customer segment, influencing both customer roadmaps and our own product direction.
- Be a Trusted Advisor: Build enduring, C\-level and Principal Engineer\-level relationships within top AI labs, becoming their go\-to expert for complex data architecture challenges.
- Mentor and Lead: Elevate the technical acumen of the entire solutions team by mentoring colleagues and defining the gold standard for supporting high\-stakes AI development.
Key Responsibilities
- Lead deep architectural design sessions with AGI/ASI research and engineering teams to co\-create data solutions that solve critical bottlenecks in the model development lifecycle.
- Architect and prototype bespoke, scalable data pipelines that meet the unprecedented scale, quality, and security requirements of foundation model training.
- Deliver compelling, technically\-grounded presentations and whiteboarding sessions that articulate how our solutions fundamentally improve research velocity and model performance.
- Lead the execution of targeted proof\-of\-concept (POC) engagements, establishing data\-driven success criteria that directly map to our customers' research objectives.
- Develop reference architectures, technical documents and best practice guides that establish our company as a thought leader in the AI data ecosystem.
- Serve as the primary technical liaison between our customers and our internal Product/Engineering teams, translating novel research needs into concrete product requirements.
Requirements
- Master’s or PhD in Computer Science, AI/ML, or a related technical field, or equivalent practical experience.
- 8\+ years of experience in a senior, customer\-facing technical role such as Solutions Architecture, Technical Consulting, or Field Engineering.
- 2\+ years of hands\-on experience architecting and building solutions in the AI/ML space, including a deep understanding of the modern ML lifecycle.
- Proven experience with Generative AI, including a deep, practical understanding of foundation models, RAG, fine\-tuning, and agentic architectures.
- Experience architecting solutions for major cloud platforms (AWS, GCP, Azure).
- Demonstrated ability to influence and build relationships with senior technical stakeholders (Principal Engineers, Research Scientists, VPs of Engineering).
- A track record of leading complex technical projects (e.g., enterprise POCs, security reviews, and architectural assessments) from discovery to implementation.
- Exceptional communication and presentation skills, with the ability to distil complex technical concepts for audiences ranging from researchers to executives.
- Own problems end\-to\-end and are willing to pick up whatever knowledge you're missing to get the job done.
- Experience working in a high\-growth AI startup or a hyperscalers AI division is a significant plus.
TELUS Values:
TELUS recognizes and embraces the importance of values in our ever\-changing workplace.
To be successful, all applicants must demonstrate behaviors that are reflective of our values:
- We passionately put our customers and communities first
- We embrace changes and innovate courageously
- We grow together through spirited teamwork
At TELUS, we are committed to diversity and equitable access to employment opportunities based on ability.
Equal Opportunity Employer:
At TELUS International, 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.
About Company:
TELUS Digital is the customer experience transformation partner to the world's most admired brands. Our diverse team weaves data, technology, and human ingenuity to deliver differentiated customer journeys, drive operational effectiveness, and scale AI solutions with meaningful value and positive impact.
We craft real\-world solutions in the moments that matter, from customer acquisition to lifelong loyalty. Enabled by our global reach \- spanning 78,000 experts in 33 countries \- and deep industry expertise, we help over 600 organizations make the customer experience feel effortless.
Our solutions span Data \& AI, Digital Experience \& IT, CX Management and Trust \& Safety. At the core of our innovation is Fuel iX™, an enterprise\-grade generative AI platform that helps clients safely access and optimize leading LLMs to scale their own AI from pilot to production.
Equal Opportunity Employer Statement
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 applicant's qualifications, merits, competence and performance without regard to any characteristic related to diversity.
Salary Context
This $150K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $150K to $160K.
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.
TELUS Digital AI Hiring
TELUS Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $160K - $160K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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