Applied AI Specialist – Data Systems
Element Fleet Voir toutes les offres
- Toronto, ON
- 76.300-104.900 $ par an
- Permanent
- Temps-plein
- Design, build, and maintain data pipelines that directly support machine learning experimentation, model training, and production inference workloads whilst collaborating with technology stakeholders.
- Prepare curated datasets for supervised and unsupervised learning use cases, including feature extraction, transformation, normalization, and labeling workflows.
- Partner with AI engineers to support algorithm development, feature engineering, and model performance optimization.
- Develop and operationalize data workflows supporting model deployment, monitoring, retraining, and version control.
- Implement data integration patterns for ML pipelines using tools such as MLflow, Airflow, dbt, and CI/CD workflows.
- Support scalable model serving environments and ensure data reliability for APIs and AI-driven applications.
- Build, maintain, and optimize batch and/or streaming ETL/ELT pipelines using SQL and Python.
- Implement monitoring and alerting for model training datasets and inference inputs (freshness, drift, anomalies).
- Independently own small AI data components or features from design through production release.
- Contribute to code reviews, Git workflows, testing practices, and technical documentation.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent practical experience).
- 1–3 years of hands-on experience (including internships/co-ops) in data engineering, machine learning systems, or software engineering.
- Demonstrated experience supporting machine learning model experimentation, training pipelines, or deployment workflows.
- Strong proficiency in Python and SQL.
- Familiarity with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
- Experience working with cloud platforms (AWS, Azure, or GCP).
- Familiarity with MLflow, Airflow, dbt, or similar orchestration/MLOps tools.
- Nice to have: familiarity with vector databases (e.g., Pinecone, Weaviate, Bedrock Knowledgebase) and graph databases (Neo4j, Neptune).
- Nice to have Familiarity with framework conversion: TensorFlow, PyTorch, TensorRT, ONNX to inference optimization.
- Communicates clearly, documents decisions, welcomes feedback, and learns quickly in an environment that expects both ownership and continuous improvement.
- A culture of innovation, empowerment, decision-making, and accountability
- Comprehensive health and welfare benefits that serve the needs of you and your family and foster a culture of wellness (for qualified roles)
- Additional benefits and amenities, including paid time-off programs (vacation, sick leave, and holidays) (for qualified roles)