Web
ML Data Engineer
Canada - Remote
Sponsorship not specified$100k-$150kDetected 7 days ago
PythonSQLVector DatabasesAWSGCPMachine LearningData EngineeringRAGMLOps
About the role
- Individual salaries are determined by various factors including, but not limited to candidate qualifications such as skills, education, and experience, as well as internal equity and market conditions
Responsibilities
- Developing API-based ingestion frameworks, event-streaming solutions, real-time and batch data pipelines, and optimizing large-scale data processing environments.
- Supports emerging AI technologies such as embeddings, vector databases, retrieval-augmented generation (RAG), and AI-driven application integration.
- Partners closely with data engineering, BI, analytics, application, and AI teams to ensure enterprise data is reliable, governed, scalable, and optimized for both reporting and AI-driven use cases.
Requirements
- Strong experience with cloud data platforms, distributed data processing, SQL and Python development, and modern data architecture patterns.
- Experience with OCI, AWS, or GCP environments, as well as exposure to AI/ML operationalization and MLOps concepts, is highly desirable.
Compensation
- The target compensation range for this position is $100k - $150k CAD annually.
Company info
- Newfold Digital is a leading web technology company serving millions of customers globally.
- Our customers know us through our robust portfolio of brands.
- We have some of the industry's most prominent and storied go-to-market brands, including Bluehost, HostGator, Domain.com, Network Solutions, Register.com and Web.com.
- We help customers of all sizes build a digital presence that delivers results.
- With our extensive product offerings and personalized support, we take pride in collaborating with our customers to serve their online presence needs.
- The strength of our company lives in the intersection of our people, our customers, and our brands.
This listing is sourced directly from Web's careers page and normalized into a canonical job model.