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. This 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. We are seeking candidates who demonstrate an active interest in emerging AI tools, agentic systems, and applied machine learning beyond traditional data engineering responsibilities. 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.

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.

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).
• 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.
• Location: Toronto

Benefits

Additional Information

Get started on an exciting career at Element! Element employees make a difference in the lives of others every day. We are re-defining the fleet management industry to be people first, then business - delivering on our promise of a superior client experience. This takes hard work and innovation, and we need more like-minded people on our team.

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Company Intel

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