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Cloud AI Engineer

Definity Financial • Waterloo, ON, Canada • 3w ago

What you’ll be doing

Implementation of DevOps and MLOps Platform principles and standards to guarantee a scalable, flexible, resilient, and secure environment
Deploying, monitoring, and maintaining Machine Learning models in multiple environments
Create business architecture models that reflect our strategies and goals
Work with Engineers, Data Scientists and Developers from other teams to recommend and implement appropriate DevOps solutions
Continuously assist with identifying and implementing improvement around automation, cloud platforms and pipelines
Collaborate in estimating delivery work necessary to realize a requirement through the development lifecycle
Communicate status of assigned work and provide timely escalation of issues to ensure objectives are met
Building and implementing new development tools and infrastructure focused around cloud technologies
Working on ways to automate and improve development and release processes
Testing and examining code written by others and analyzing results
Ensuring that systems are safe and secure against cybersecurity threats and vulnerabilities
Identifying technical problems and developing software updates and fixes
Working with software developers and software engineers to ensure that development follows established processes and works as intended
Planning projects and being involved in project management decisions
Designing and implementing the tools and infrastructure that support the DevOps pipeline
Streamline the software development and deployment pipeline
Identify, evaluate, and recommend DevOps tools that align with project requirements and goals
Develop and maintain automation scripts and workflows to streamline deployment and operations processes

The key technology in our environment include:

Cloud Platforms: AWS, GCP
DevOps Technologies: JFrog Artifactory, Vault, Terraform, Jenkins, Bitbucket Pipelines, ArgoCD
Scripting and Programming Languages: Bash, Python, Node, Ruby, Go
Container Platforms: Kubernetes, EKS, GKE, Anthos, Docker
Machine Learning Technologies: Vertex AI, Dialogflow, Gemini

What you bring

5+ years combination of solution design, technical implementation, and infrastructure architecture experience
Strong background in MLOps practices across a multi-cloud environment
Experience with application architectures, middleware, and technology platform architectures (cloud, on-premise, and hybrid)
Proficiency with Git and Git workflows
Experience with model lifecycle management and machine learning principles
Previous work on implementing multiple complex enterprise level changes simultaneously, creating standards, and promoting and measuring adoption
Experience deploying full-stack solutions and building development capabilities on cloud platforms (AWS and GCP)
Everything as Code, DevOps everything mindset
Professional level cloud certification preferred
Bachelor of science degree (or equivalent) in computer science, engineering, or relevant field

Role ID 572