Senior Consultant, Ml/Ai Engineer, Data & Ai
Toast
Job DescriptionToast 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 includes 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
#J-18808-Ljbffr
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 includes 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
#J-18808-Ljbffr
Vacancy posted 5 hours ago
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