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Added 14 mins ago
BG
Purchasing Manager - Power Systems
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Zehner
- Full-time
A minimum of 6 years of professional experience ideally within Procurement or Supply Chain environments.
Added 14 mins ago
BG
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Added 14 mins ago
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Added 14 mins ago
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Added 14 mins ago
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Added 14 mins ago
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Added 14 mins ago
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Toast

Senior Consultant, ML/AI Engineer, Data & AI

Vancouver, BC, Canada

$77,000 to $102,000 USD / year · Full-time

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Job details

Pay: $77,000 to $102,000 USD / year
Job type: Full-time
Apply by: Nov 26, 2026
Experience: 3+ years of professional experience in machine learning, data science, AI engineering, or a related field, with a track record of delivering production ML solutions
Posting language: English
Posted: Oct 27, 2026

Original job description

Job Description

Toast is recruiting on behalf of a global professional services firm with a large Canadian presence and thousands of professionals across the country, for a Senior Consultant, ML/AI Engineer to join its Data & AI practice in Vancouver. The firm helps some of Canada’s largest organizations solve complex business challenges, and its Data & AI team builds and scales machine learning and generative AI solutions for clients across industries. In this hands-on, client-facing role, you will turn advanced analytics, ML, and GenAI use cases into secure, production-ready solutions across on-prem and cloud environments, with a focus on Azure alongside GCP and AWS.

Responsibilities

Partner with clients to understand their business problems and identify where AI and advanced analytics can make an impact
Translate business and analytical requirements into end-to-end ML/AI solution designs
Carry out ML/AI engineering work including exploratory data analysis, data preparation, and model development (forecasting, classification, recommendation, anomaly detection) using Python and frameworks such as scikit-learn, TensorFlow, PyTorch, Azure ML Studio, and Databricks MLflow
Build and optimize AI and GenAI solutions using platforms such as AI Foundry, GCP Vertex AI, AWS SageMaker, and Bedrock
Operationalize AI/ML pipelines using MLOps best practices, including model deployment, versioning, CI/CD, automated testing, and monitoring
Implement model monitoring, performance tuning, drift detection, and retraining strategies in production
Work with data engineers to ensure reliable, scalable data pipelines that support model training and inference
Apply responsible AI principles including explainability, bias detection, model governance, and security and privacy compliance
Support client workshops, technical discussions, and stakeholder presentations on AI strategy, solution design, and implementation

Requirements

University degree in computer science, engineering, data science, mathematics, or a related field
3+ years of professional experience in machine learning, data science, AI engineering, or a related field, with a track record of delivering production ML solutions
Strong Python skills for data analysis, machine learning, and model development
Hands-on experience with ML frameworks and platform tools such as scikit-learn, TensorFlow, PyTorch, Azure ML Studio, and Databricks MLflow
Solid understanding of ML algorithms, statistics, model evaluation, and feature engineering
Experience designing and building end-to-end ML pipelines, from data preprocessing and training through validation, deployment, and monitoring
Practical MLOps experience including CI/CD, model versioning, experiment tracking, and automated retraining
Experience deploying ML models to Azure, AWS, or GCP with an understanding of cloud-native architecture and security principles
Experience with generative AI, LLMs, prompt engineering, or retrieval-augmented generation (RAG)
Strong consulting and communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
Proven ability to collaborate within cross-functional, multi-disciplinary teams

Nice to haves

Familiarity with big data or distributed processing frameworks such as Spark is considered an asset
Experience fine-tuning foundational models would be a plus
Cloud AI/ML certifications such as Azure AI Engineer Associate, AWS Machine Learning Specialty, Google Professional ML Engineer, Databricks ML Engineer Associate, or Databricks Generative AI Engineer would be a plus

Benefits

Compensation listed on the posting is for base salary. Total compensations inludes base salary + annual bonuses.
A comprehensive total rewards program covering health, wellness, and career support
Put AI into production for some of Canada’s largest organizations, with your models shaping real decisions in forecasting, risk, and customer experience
Build hands-on depth across all three major clouds (Azure, GCP, AWS) and the latest GenAI platforms in a single role
Help set the standard for responsible AI in Canada by building explainability, bias detection, and governance into every solution you ship
Grow your career inside a firm that supports learning at every stage, with room to move from engineering into AI strategy and client leadership
Application Review

Submit your application, including your resume and cover letter, through our online portal. Our team will carefully review your qualifications and experiences. We take pride in responding promptly, so if your application aligns with our current opportunities, you’ll hear back from us with next steps. Your journey with Toast begins here, and we’re excited to see what you bring to the table!

Vetting Call

If your application is successful, we’ll schedule a vetting call with a member of the Toast team. This 30-45 minute informal conversation is designed for us to get to know you better and assess your skills. Be prepared to discuss your past projects, the challenges you’ve tackled, and the impact you’ve made. This is your chance to shine and show us why you’re a great fit for the roles we offer.

Profile Creation

Once you’ve impressed us in the vetting call, we’ll create an unbiased profile that highlights your experience while minimizing common biases. Instead, we focus on your skills, industry experience, and the impact you’ve made. This way, your profile reflects your true potential and accomplishments.

Client Submission

After crafting your unbiased profile, we submit it to our trusted clients. We work closely with these clients to ensure they understand the value you bring. Throughout their interview process, we remain by your side, providing support and guidance. Our goal is to help you navigate the hiring process smoothly and confidently, ensuring you have the best chance to secure a role that fits your skills and aspirations.

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More about this job

Responsibilities

Partner with clients to translate business needs into secure, production-ready machine learning and generative AI solutions, including data preparation, model development, and deployment across Azure, GCP, and AWS. Build and operate MLOps pipelines, monitor and improve models in production, apply responsible AI practices, and support client workshops and stakeholder discussions.

Requirements

Requires a university degree in computer science, engineering, data science, mathematics, or a related field, plus at least three years of relevant professional experience delivering production machine learning solutions. Candidates need strong Python and ML skills, end-to-end pipeline and MLOps experience, cloud deployment knowledge, familiarity with generative AI, and strong consulting, communication, and collaboration abilities.

Skills

Python
Machine Learning
Generative AI
Scikit-Learn
TensorFlow
PyTorch
Azure Machine Learning
Databricks MLflow
MLOps
Cloud Computing
Model Deployment
CI/CD
Model Monitoring
Feature Engineering
Retrieval-Augmented Generation
Responsible AI

Benefits

Health And Wellness Support
Career Support
Annual Bonuses
Learning And Career Development

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Artificial Intelligence
Generative AI
Python
Scikit-Learn
TensorFlow
PyTorch
Azure Machine Learning
Databricks MLflow
AI Foundry
Google Vertex AI
AWS SageMaker
Amazon Bedrock
MLOps
CI/CD
Model Versioning
Experiment Tracking
Automated Testing
Model Monitoring
Drift Detection
Retraining
Feature Engineering
Forecasting
Classification
Recommendation Systems
Anomaly Detection
Large Language Models
Prompt Engineering
Retrieval-Augmented Generation
Responsible AI

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