AI Engineer
CAN, Ontario, Toronto, 200 Bloor Street East, Canada
CAD 94K-144K Mid-level Full Time
Tasks
- Build maintain machine learning pipelines
- Build prompt frameworks templates
- Build prototypes using Python APIs vector databases embeddings
- Conduct model experimentation
- Create scoring rubrics benchmark datasets
- Design evaluation methodologies
- Develop configure test GenAI solutions
- Develop deploy machine learning models in production
- Develop integrate embeddings vector search knowledge bases
- Develop train deploy machine learning models
- Evaluate compare large language models
- Implement MLOps best practices
- Implement RAG systems with vector databases
- Integrate retrieval-augmented generation
- Prepare transcripts metadata knowledge sources
- Run model comparisons dashboards
- Test for compliance risk privacy
- Use Azure OpenAI LangChain Semantic Kernel
- Use ML frameworks for training
- Use MLOps tools for lifecycle management
Perks/Benefits
- Employee assistance program
- Health, dental, vision benefits
- Hybrid work environment
- Life and ADandD insurance
- Paid time off
- Retirement savings plan
- Wellness benefits
Skills/Tech-stack
API Integration | Azure ML | CI/CD | Deep learning | Drift Detection | Embeddings | Generative AI | Kubeflow | LLM Evaluation | Langchain | Language Models | Language Processing | Large Language Models | MLflow | Machine Learning | Model Monitoring | Model versioning | NLP | Natural Language | Natural Language Processing | NumPy | Pandas | Prompt engineering | PyTorch | Python | RAG | Retrieval-Augmented Generation | Scikit-learn | Semantic Kernel | TensorFlow | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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