Machine Learning Engineer

Résumé du poste

Build, deploy, and optimize machine learning models for production systems. Responsibilities include maintaining ML pipelines, exposing models via APIs, and collaborating with data and engineering teams.

Détails du poste

Build, deploy and optimise machine-learning models for MailerMen production systems, based in Toronto on a hybrid schedule. Responsibilities Build and deploy machine-learning models Optimise model performance Maintain ML pipelines Expose models through APIs Monitor models in production Collaborate with data and engineering teams Requirements 3-5 years of ML engineering experience Strong Python skills PyTorch or TensorFlow ML pipeline experience Cloud platform experience Skills PythonPyTorchTensorFlowML PipelinesAPIsCloud Benefits Flexible working arrangements Competitive local market compensation Contract opportunity with potential extension Professional development opportunities Exposure to international technology projects Collaborative and diverse working environment Related searches AI & Prompt Engineering Hybrid Jobs in CanadaAll AI & Prompt Engineering jobsJobs in Canada

Ce que vous ferez

Build, deploy, and optimize machine learning models for production systems. Responsibilities include maintaining ML pipelines, exposing models via APIs, and collaborating with data and engineering teams.

Exigences

Requires 3-5 years of machine learning engineering experience with strong Python skills. Proficiency in PyTorch or TensorFlow, ML pipelines, and cloud platforms is necessary.

Avantages

• Flexible working arrangements • Competitive local market compensation • Professional development opportunities • Exposure to international technology projects • Collaborative and diverse working environment

Compétences indiquées

• Python · Souhaitée

Autres compétences pertinentes

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Python
• PyTorch
• TensorFlow
• ML Pipelines
• APIs
• Cloud Platforms

Domaines d’emploi

• Technology
• Software
• Data & Analytics
• Engineering
• Consulting