Ingénieur en machine learning/Machine Learning Engineer, ProServe Shared Delivery Team - Data & AI

Êtes‑vous enthousiaste à l’idée de créer des solutions logicielles autour de grands systèmes complexes d’apprentissage automatique (AA) et d’intelligence artificielle (IA) ? Souhaitez‑vous aider les plus grandes entreprises mondiales à tirer une valeur commerciale de l’adoption et de l’automatisation de l’IA générative ? Êtes‑vous motivé à utiliser d’énormes volumes de données hétérogènes pour développer des modèles d’IA / AA ? Avez‑vous envie d’apprendre à appliquer l’IA / AA à une grande diversité de cas d’usage en entreprise ? Vous rejoignez Amazon Web Services (AWS), un leader mondial de l’innovation en cloud et en IA .

Are you excited about building software solutions around large, complex Machine Learning (ML) and Artificial Intelligence (AI) systems? Want to help the largest global enterprises derive business value through the adoption and automation of Generative AI (GenAI)? Excited by using massive amounts of disparate data to develop AI/ML models? Eager to learn to apply AI/ML to a diverse array of enterprise use? Thrilled to be a key part of Amazon, who has been investing in Machine Learning for decades─pioneering and shaping the world’s AI technology?

Key job responsibilities

• Implement end-to-end AI/ML and GenAI projects: understand business needs, prepare data, develop models, deploy and monitor solutions.
• Design and implement machine learning pipelines that support high‑performance, reliable, scalable, and secure ML workloads.
• Design scalable ML solutions and operations (MLOps) using AWS services and leverage GenAI solutions when applicable.
• Collaborate with cross‑functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ML models.
• Serve as a trusted advisor to customers on AI/ML and GenAI solutions and cloud architectures.
• Share knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts.
• Ensure compliance with industry standards, and support customers in advancing their AI/ML, GenAI, and cloud adoption strategies.
• Travel to customer sites as needed.

Basic Qualifications

• Experience implementing AWS services in distributed computing environments.
• 5+ years of experience in cloud architecture and implementation.
• 5+ years of experience in data, software, or machine learning engineering, with a strong understanding of distributed computing (e.g., data pipelines, distributed training and inference, ML infrastructure design).
• 3+ years of developing platforms for predictive modeling, natural language processing, and deep learning, with a proven track record of building, hosting and deploying machine learning models on cloud services (e.g., Amazon SageMaker).
• 3+ years of development with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, TypeScript). Proficiency with industry‑leading ML libraries and frameworks such as TensorFlow and PyTorch.
• Bilingualism in French and English is required for this position if the candidate is located in Quebec.

Preferred Qualifications

• 5+ years of IT implementation experience.
• Experience and technical expertise in cloud computing technologies.
• Experience leading the design, development and deployment of business software at scale or recent hands‑on technology infrastructure, network, compute, storage, and virtualization experience.
• Experience with AWS services (e.g., SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, Step Functions, VPC, CloudFormation).
• AWS Professional level certifications (e.g., Solutions Architect Professional, DevOps Engineer Professional).
• Experience with automation and scripting (e.g., Terraform, Python).
• Knowledge of common security and compliance standards (e.g., HIPAA, GDPR).
• Strong communication skills to explain technical concepts to both technical and non‑technical audiences.
• Experience building ML pipelines with best MLOps practices, including data preprocessing, model hosting, feature selection, hyperparameter tuning, distributed training, GPU training, deployment, monitoring, and retraining.
• Experience with MLOps tools (e.g., MLFlow, Kubeflow) and orchestration tools (e.g., Airflow, AWS Step Functions). Experience developing applications using Generative AI tools and technologies (LLMs, Vector Stores, Orchestrators such as LangChain, Prompt Engineering). Experience with Infrastructure as Code (e.g., CloudFormation, CDK, Terraform), containers and CI/CD pipelines.

Benefits & Compensation

Base salary ranges by location:
CAN, AB, Calgary – 99,900.00 – 166,900.00 CAD annually
CAN, BC, Vancouver – 99,900.00 – 166,900.00 CAD annually
CAN, ON, Toronto – 99,900.00 – 166,900.00 CAD annually
CAN, QC, Montreal – 99,900.00 – 166,900.00 CAD annually

Amazon is a total‑compensation company. Your package may include other elements such as sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location.

Benefits include health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well‑being.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Amazon est un employeur garantissant l’égalité des chances et ne fait aucune discrimination sur la base du statut d’ancien combattant protégé, d’un handicap ou de tout autre statut protégé par la loi.

Job ID: A10456490