Levio

Levio

ML/AI Engineer

Toronto

Sponsorship not specified$110k-$150kDetected 12 days ago
PythonAWSCI/CDMachine LearningTensorFlowPyTorchscikit-learnData EngineeringData ScienceLLMsRAG

About the role

  • We are seeking ML/AI Engineers to contribute to major projects.
  • Work on complex, high impact digital transformation projects
  • Enjoy flexibility, autonomy, and a strong people first culture

Responsibilities

  • This role owns the end-to-end ML lifecycle, ensuring models and AI services are scalable, reliable, secure, and deliver measurable business value.
  • Collaborate with experienced, multidisciplinary teams
  • Continuously develop your technical and professional expertise
  • Build training, validation, and inference pipelines for ML and LLM-based solutions
  • Implement feature engineering, embeddings, and model versioning best practices
  • Support LLM-based systems, including RAG architectures and inference optimization
  • Implement CI/CD for ML and LLM workflows, including automated deployment and rollback
  • Collaborate with AI Architects, Developers, and Data Engineers across delivery teams

Requirements

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field
  • 6+ years of experience in engineering roles, with 3+ years in AI/ML positions
  • Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • Experience with cloud ML platforms, especially AWS SageMaker
  • Hands-on experience deploying and operating ML models in production
  • Strong understanding of data pipelines, model lifecycle management, and monitoring

Compensation

  • $110,000 to $150,000 per year.

Benefits

  • Levio offers a comprehensive and flexible benefits
  • The ML / AI Engineer design, build, deploy, and operate production-grade machine learning and generative AI systems.
  • Design, implement, and productionize machine learning and generative AI models
  • Benefits and Work Environment

This listing is sourced directly from Levio's careers page and normalized into a canonical job model.