Applied AI Specialist – Data Systems

About The Role

We’re looking for a detail-oriented, reliability-focused, and methodical professional to join our team as Applied AI Specialist – Data Systems. In this role, you will build and maintain production-grade data foundations that directly enable machine learning model training, experimentation, and deployment. The role independently owns small AI-ready datasets, feature pipelines, and data services that power AI-enabled products, ensuring reliable, scalable, and well-governed data for model development and inference in production environments.

While this role has a core focus on AI-ready data foundations, the expectation is that team members are adaptable and willing to expand into adjacent AI engineering work, including supporting agent development and evolving AI product needs as priorities shift.

• 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, Tensor Flow, 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 – Tensor Flow, 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.

Toronto

The hiring base salary range for this position is 76,300 – 104,900 annually. Actual compensation within this range will be dependent upon the individual’s knowledge, skills, experience, equity with other team members, and alignment with market data. Please note that the disclosed salary range is solely for candidates hired to perform work within this geographic location. Candidates hired to work in other locations will be subject to the pay range associated with that location.

• 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).

Element Fleet Management and its wholly owned subsidiaries are an equal opportunity employer committed to diversity, equity, inclusion, and belonging. We are…