ML/AI Engineer
Tasks
- Build generative AI RAG systems
- Build training validation inference pipelines
- Collaborate with architects developers and data engineers
- Design production machine learning models
- Ensure security, privacy, and responsible AI compliance
- Implement CI/CD for ML workflows
- Implement feature engineering and embeddings
- Mentor junior ML AI engineers
- Monitor model performance drift bias and reliability
- Optimize LLM inference
- Optimize compute latency and operational costs
- Version and deploy models
Perks/Benefits
- Employer RRSP contribution
- Flexible working hours
- Health insurance
- Personal days
- Professional development allowance
- Referral bonuses
- Remote work
- Statutory Holidays
- Training
- Transportation reimbursement
- Vacation
Skills/Tech-stack
AWS SageMaker | CI/CD | Data Pipelines | Embeddings | Feature Engineering | Generative AI | Inference Optimization | Language Models | Large Language Models | Lifecycle Management | Machine Learning | Model Lifecycle | Model Lifecycle Management | Model Monitoring | Model versioning | Privacy | PyTorch | Python | Responsible AI | Retrieval-Augmented Generation | Scikit-learn | Security | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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