2 months ago
Toronto, CanadaSenior
Responsibilities
- Design, implement, and productionize machine learning and generative AI models.
- Build training, validation, and inference pipelines for ML and LLM-based solutions.
- Implement feature engineering, embeddings, model versioning, CI/CD, automated deployment, and rollback practices.
- Support LLM-based systems, including RAG architectures and inference optimization.
- Monitor AI systems for performance, drift, bias, and production reliability.
- Optimize compute usage, latency, and operational costs across AI workloads.
- Ensure AI systems meet security, privacy, and responsible-AI standards.
- Collaborate with AI Architects, Developers, and Data Engineers across delivery teams.
- Mentor junior ML/AI engineers and contribute to engineering best practices.
Requirements
- Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, or a related field.
- 6+ years of experience in engineering roles, including 3+ years in AI/ML positions.
- Strong proficiency in Python and ML frameworks including PyTorch, TensorFlow, and scikit-learn.
- Hands-on experience deploying and operating ML models in production.
- Experience with cloud ML platforms, especially AWS SageMaker.
- Strong understanding of data pipelines, model lifecycle management, and monitoring.
Benefits
- Remote work arrangement.
- Full-time, permanent employment.
- Four weeks of cumulative vacation starting from day one.
- Flexible working hours.
- Professional Development Allowance for training, computer equipment, and physical activities.
- Training tailored to areas of expertise.
- RRSP with employer contributions up to 3% of gross salary.
- Modular group insurance plan.
- Public transportation or parking reimbursement when required.
- Referral bonuses, 11 statutory holidays, personal days, and social activities.
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