Applied AI Specialist – Data Systems

Element Fleet Management • Toronto, ON

1d • CA$76,300 - CA$104,900 • Onsite

About The Position

Element is seeking a detail-oriented, reliability-focused, and methodical professional to join their team as an Applied AI Specialist – Data Systems. This role involves building and maintaining production-grade data foundations essential for machine learning model training, experimentation, and deployment. The specialist will independently manage 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. Candidates should have an active interest in emerging AI tools, agentic systems, and applied machine learning beyond traditional data engineering. While the core focus is on AI-ready data foundations, team members are expected to be adaptable and willing to engage in adjacent AI engineering work, including supporting agent development and evolving AI product needs.

Requirements

• 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).

Nice To Haves

• Familiarity with MLflow, Airflow, dbt, or similar orchestration/MLOps tools.
• Familiarity with vector databases (e.g., Pinecone, Weaviate, Bedrock Knowledgebase) and graph databases (Neo4j, Neptune).
• 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.

Responsibilities

• 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.

Benefits

• 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)

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level