AI Engineer
CAN, Ontario, Toronto, 200 Bloor Street East, Canada
CAD 94K-144K Mid-level Full Time
Tasks
- Apply MLOps best practices
- Build and maintain machine learning pipelines
- Build prompt frameworks and templates
- Build prototypes with Python and APIs
- Create scoring rubrics and benchmark datasets
- Design evaluation methodologies
- Develop and deploy ML models in production
- Develop and test GenAI solutions for contact center workflows
- Develop, train, and deploy machine learning models
- Evaluate and compare LLM capabilities
- Integrate LLMs with enterprise data sources using RAG
- Prepare transcripts metadata and knowledge sources for GenAI
- Run iterative model experimentation
- Run model comparisons and maintain dashboards
- Test solutions for compliance risk and privacy
- Use ML frameworks for model development
- Use MLOps tools for training deployment and monitoring
Perks/Benefits
- Employee family assistance plans
- Financial education and counseling
- Health, dental, vision insurance
- Hybrid work environment
- Incentive programs
- Mental health support
- Paid time off
- Retirement savings plan
- Wellness benefits
Skills/Tech-stack
APIs | AWS Bedrock | Anthropic | Azure AI | Azure Machine Learning | Azure OpenAI | Azure OpenAI Service | CI/CD | ChromaDB | Deep learning | Drift Detection | Embeddings | Google Cloud | Kubeflow | Langchain | Language Models | Language Processing | Large Language Models | MLOps | MLflow | Machine Learning | Milvus | Model Drift | Model Drift Detection | Model Monitoring | Model versioning | NLP | Natural Language | Natural Language Processing | NumPy | OpenAI | OpenAI Service | Pandas | Pinecone | 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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