Senior Engineer - Generative AI

Rubicon Path; Toronto; Sur place; CAD 100 000 - 140 000; Plein temps

Résumé du poste

A leading technology firm in Toronto is seeking a Senior Engineer specializing in Generative AI. The candidate should have over 8 years of experience in data-intensive solutions and strong programming skills in languages such as Python and Go. Responsibilities include architecting APIs for AI models, ensuring scalability, and implementing safety measures. Ideal candidates will have a Bachelor’s degree in a technical field.

Qualifications

• 8+ years of experience designing and building data-intensive solutions.
• 4+ years of programming experience with Python, Go, Scala, or Java.
• 3+ years of experience building APIs for deep neural networks.

Responsabilités

• Architect and build core APIs for accessing AI models.
• Design APIs for performance and scalability.
• Develop tools to monitor API usage and operational health.

Connaissances

Experience with Generative AI

Distributed computing

Python

API design

Deep neural networks

Formation

Bachelor's degree in Computer Science or related field

Description du poste

About the job Senior Engineer - Generative AI

Our client in the financial service space in Toronto is looking for a Senior Engineer with experience in Generative AI.

Qualifications:

• Bachelors degree in Computer Science, Computer Engineering, or a related technical field
• Minimum of 8 years of experience designing and building data-intensive solutions using distributed computing and cache optimization techniques
• Minimum of 4 years of programming experience with Python, Go, Scala, or Java
• Minimum of 3 years of experience building, scaling, and optimizing training and inferencing APIs for deep neural networks
• Architecting, building, and deploying well-managed core APIs and SDKs for accessing AI models
• Designing APIs for performance, scalability, real-time applications, ease of use, and automation
• Developing application-specific interfaces that enhance customer experiences using AI models
• Enabling users to build new AI capabilities
• Creating tools and processes to monitor API usage and ensure operational health
• Implementing AI safety and guardrails in the API layer