Associate Data Engineer-MLOps

Gore Mutual InsuranceCambridge, Ontario, Canada; Toronto, Ontario, Canada$58k – $95k1w ago

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Requirements

A graduate degree in a technical field with specialization in Analytics, Data Science, or a related subject
Proficiency in Python and SQL.
Excellent understanding of Object Oriented and Functional programming paradigms.
Knowledge of data pipeline frameworks (e.g., Databricks) and MLOps tools (e.g., MLflow, Kubeflow).
Experience developing Extract-Transform-Load processes using Spark.
Experience with MLOps/AgentOps architecture, data modeling, and database systems.
Experience working with and analyzing structured, semi-structured, and unstructured datasets.
Knowledge of data modelling approaches and best practices
Familiarity with the Azure platform; knowledge of core data services such as Data Factory, Event Hubs, Databricks, Data Warehouse, etc
High attention to detail and a commitment to delivering high-quality solutions.
Exceptional problem-solving and analytical skills.
Azure certification
Experience with large scale data migration projects.
The expected base salary range for this position is $ 57,500 - $ 95,000 Depending on your relevant experience, skills, qualifications, market conditions and business needs, base compensation may vary. You have the potential to earn more through Gore Mutual’s discretionary bonus program which gives you an opportunity to increase your total compensation, provided the business meets its performance targets and you meet your individual goals.

AI/ML & Data Pipeline / AI/ML Model Deployment:
Design, build, and maintain pipelines that support the development and maintenance of AI/ML models, data ingestion, transformation, and storage of large-scale datasets.
Optimize data workflows to ensure efficient data processing and availability.
Develop and implement best practices for deploying, monitoring, and managing robust data and analytics pipelines and AI/ML models in production.
Collaborate with data scientists to ensure seamless integration and deployment of AI/ML solutions.
Support internal business clients such as Data Scientists and Visualization Specialists with their various data needs in collaboration with other Data Engineering team members.
Security and Compliance
Ensure that data engineering and MLOps/AgentOps processes adhere to industry standards, regulatory requirements, and security best practices.
Implement measures to safeguard sensitive data and protect against potential vulnerabilities