Manager, ML/AI Engineer, Data & AI

Toronto, ON
On-site
Posted Oct 14, 2026
1 position

US$101,000–US$135,000 / year

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Employment type: Full-time
Experience level: Senior · 5+ years
Minimum education: Bachelor’s degree
Posting language: English
Working hours: 40 hours per week
Seniority: Mid-Senior level

Job summary

Partner with clients to identify AI opportunities and design, build, and deploy end-to-end machine learning and generative AI solutions across cloud and on-premises environments. Establish production-grade ML pipelines and monitoring, maintain model performance and governance safeguards, and lead client workshops on AI strategy and implementation.

Job details

Job Description Toast is recruiting on behalf of a global professional services firm with more than 10,000 people across Canada, serving many of the country’s largest enterprises and public sector organizations. Its AI team helps clients take machine learning and generative AI from proof of concept to secure, scalable production. They are hiring a Manager, ML/AI Engineer to work directly with clients, architect AI solutions end to end, and get them running reliably in the cloud and on-prem. This is a full-time, hands-on role based in Toronto, Vancouver, or Calgary. Responsibilities Work alongside client teams to pinpoint business problems that AI and advanced analytics can solve Turn client goals into complete ML/AI solution architectures, from data to deployment Build models for real business needs like demand forecasting, recommendations, classification, and spotting anomalies, starting with data exploration and preparation Design and refine generative AI solutions on leading cloud AI platforms across Azure, Google Cloud, and AWS Set up production-grade ML pipelines with automated testing, version control, continuous deployment, and monitoring Keep models healthy after launch by tracking performance, catching drift, and planning retraining Team up with data engineers so training and inference run on dependable, scalable data foundations Build fairness, transparency, governance, and privacy safeguards into every solution Lead and contribute to client workshops and presentations on AI strategy and implementation Requirements Degree in computer science, engineering, data science, math, or a similar field 5+ years in machine learning, data science, or AI engineering, including shipping ML solutions to production Advanced Python for analysis, modelling, and engineering work Hands-on experience with popular ML libraries and platforms such as PyTorch, TensorFlow, scikit-learn, Databricks, or Azure ML Strong foundation in statistics, ML algorithms, feature engineering, and model evaluation Track record building full ML pipelines covering preprocessing, training, validation, deployment, and monitoring Working knowledge of MLOps, including experiment tracking, model versioning, CI/CD, and automated retraining Experience deploying models on Azure, AWS, or GCP, with a solid understanding of cloud architecture and security Direct experience with LLMs and generative AI, such as prompt engineering or RAG Client-facing confidence and the ability to make technical ideas clear to business audiences Comfortable working across disciplines to solve complex problems as a team Nice to Haves Experience with Spark or other distributed processing tools would be a plus Experience fine-tuning foundation models is considered an asset A cloud AI or ML certification from Azure, AWS, Google Cloud, or Databricks would be a plus Benefits Base salary of $101,000 to $135,000, depending on location, skills, and experience Eligibility for annual bonus awards on top of base salary Full benefits package, including health and wellness coverage Choice of three major Canadian tech hubs: Toronto, Vancouver, or Calgary Build production AI systems that large Canadian organizations rely on to make decisions every day, from forecasting demand to flagging anomalies before they become costly problems Shape how major organizations adopt AI responsibly, embedding explainability, bias detection, and governance into the systems you deliver Hands-on exposure to all three major clouds and the latest GenAI tooling, building a skill set that stays in high demand A manager-level seat with direct client access, giving you a strong runway toward senior leadership in AI consulting 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.

What you’ll do

Requirements

Requires a degree in computer science, engineering, data science, mathematics, or a related field, along with at least five years of relevant experience delivering machine learning solutions to production. Candidates need advanced Python, hands-on ML and cloud platform experience, strong MLOps and modeling foundations, direct generative AI experience, and the ability to communicate effectively with clients and multidisciplinary teams.