Senior Cloud Engineer, Computer Vision Infrastructure

Brambles•Mississauga, ON

56d•Hybrid

About The Position

CHEP helps move more goods to more people, in more places than any other organization on earth via our 347 million pallets, crates and containers. We employ approximately 13,000 people and operate in 60 countries. Through our pioneering and sustainable share-and-reuse business model, the world’s biggest brands trust us to help them transport their goods more efficiently, safely and with less environmental impact. What does that mean for you? You’ll join an international organization big enough to take you anywhere, and small enough to get you there sooner. You’ll help change how goods get to market and contribute to global sustainability. You’ll be empowered to bring your authentic self to work and be surrounded by diverse and driven professionals. And you can maximize your work-life balance and flexibility through our Hybrid Work Model. Job Description Key Responsibilities May Include: Design, orchestrate, and maintain scalable cloud infrastructure, working with engineers to automate processes and improve efficiency. Collaborate with Innovation Squads, Product Success, and other teams to maintain fast, reliable, and resilient CI/CD pipelines, empowering engineers to self-service their infrastructure needs. Support the development, testing, and maintenance of disaster recovery scenarios to ensure system availability and business continuity. Develop and deploy automated tools that enhance the developer experience, simplifying infrastructure management and deployment processes. Monitor performance, capacity, and availability of systems and infrastructure, working cross-functionally to troubleshoot and resolve platform-related issues. Create and maintain technical documentation, ensuring it is fit for use in design reviews, incident response, and support processes. Ensure best practices in cloud security, governance, and compliance are implemented across all cloud platform services. Stay up-to-date on emerging cloud technologies and trends, applying knowledge to continuously improve the scalability and performance of cloud platforms. POSITION PURPOSE Cloud Engineers set up, operate, develop, evolve, and maintain cloud-centric platform(s) including IoT, Customer Solution Delivery, Data Science Environments and Software Delivery, as well as satellite tools and environments (file storage, databases, frameworks for data streaming, eventing, machine learning and big data, data science notebook technologies, and container orchestration tooling, taking into account reliability, monitoring and cost SCOPE • Global MAJOR / KEY ACCOUNTABILITIES • Responsible for experimenting with and implementing machine learning frameworks for data science/machine learning development and operations • This person will be dedicated to the Computer Vision Data Science team to support serialization related machine learning infrastructure • Responsible for learning and operating new data science frameworks and technologies and exploring their viability for current and planned projects • Responsible for learning and operating data storage frameworks and technologies and exploring their viability for current and planned projects • Responsible for rigorous testing of framework robustness and scalability • Will contribute to data science teams and the engineering teams discussions, providing insight as needed on other team member’s current approaches and methods as well as on tools and data repositories. • Liaise with Cloud Team (Global IT) to understand corporate-wide cloud standards and policies and ensure compliance • Supporting Serialization and Asset digitization programs across • Responsible for the Continuous Integration and Deployment pipelines to support data science learning and production software delivery • Responsible for contributing to capability building of the Cloud Engineering team, including researching and staying up-to-date on best practices e.g. GitOps, IaC (infrastructure as code).

Requirements

5 years relevant experience in Cloud Engineering or adjacent fields
Installed, operated, and managed several data science and machine learning frameworks, or developed own data science methodologies
Experience with Continuous Integration and Continuous Deployment
Experience operating, optimizing, querying, and administering databases (such as Iceberg, Postgres, Patroni, DuckDB, TimescaleDB, etc.)
Comfortable using and working in a polyglot computer language environment (Python, Go, Julia etc.)
Experience with Amazon Web Services (S3, EKS, ECR, EMR, etc.)
Experience with containers and orchestration (e.g. Docker, Kubernetes)
Experience with Big Data processing technologies (Kubeflow, Spark, Hadoop, Flink etc)
Experience with interactive notebooks (e.g. JupyterHub, Databricks)
Experience with Git Ops style automation
Experience with ix (e.g, Linux, BSD, etc.) tooling and scripting
Participated in projects that are based on data science methodologies, and/or physical experiments, or statistical analysis – especially in a data engineer and dev ops capacity.
Knowledge of major data science and dev ops frameworks and methods
Very strong analytical skills and systems thinking
Strong programming skills in addition to operational skills a plus (ideally in one or more of the following languages: Python, Go, Julia, or C/C++)
Attention to big picture and details
Adaptability
Agile Methodology
Amazon Web Services (AWS)
Automation Cloud
Cloud Infrastructure
Continuous Integration and Continuous Delivery Methodologies
Cost Optimization
Data Analysis
Disaster recovery and high availability
Docker (Software)
Empathy
Experimentation
GitHub
Infrastructure As Code (IaC)
Kubernetes
Lambda
Linux
NoSQL
Prometheus
Python (Programming Language)
Scala (Programming Language)
Spark
Structured Query Language (SQL)
Systems Programming

Nice To Haves

Proven experience with looking after data science environments
Proven experience with looking after data storage systems with high availability and database tuning
Proven experience with FinOps and being able to optimize spend for CE impact
Experience with working with IoT and Edge interaction with the Cloud
BS degree in Data Science, Computer Science, Engineering, Math, Statistics, Physics, or similar formal training or equivalent

Responsibilities

Design, orchestrate, and maintain scalable cloud infrastructure, working with engineers to automate processes and improve efficiency.
Collaborate with Innovation Squads, Product Success, and other teams to maintain fast, reliable, and resilient CI/CD pipelines, empowering engineers to self-service their infrastructure needs.
Support the development, testing, and maintenance of disaster recovery scenarios to ensure system availability and business continuity.
Develop and deploy automated tools that enhance the developer experience, simplifying infrastructure management and deployment processes.
Monitor performance, capacity, and availability of systems and infrastructure, working cross-functionally to troubleshoot and resolve platform-related issues.
Create and maintain technical documentation, ensuring it is fit for use in design reviews, incident response, and support processes.
Ensure best practices in cloud security, governance, and compliance are implemented across all cloud platform services.
Stay up-to-date on emerging cloud technologies and trends, applying knowledge to continuously improve the scalability and performance of cloud platforms.
Responsible for experimenting with and implementing machine learning frameworks for data science/machine learning development and operations
This person will be dedicated to the Computer Vision Data Science team to support serialization related machine learning infrastructure
Responsible for learning and operating new data science frameworks and technologies and exploring their viability for current and planned projects
Responsible for learning and operating data storage frameworks and technologies and exploring their viability for current and planned projects
Responsible for rigorous testing of framework robustness and scalability
Will contribute to data science teams and the engineering teams discussions, providing insight as needed on other team member’s current approaches and methods as well as on tools and data repositories.
Liaise with Cloud Team (Global IT) to understand corporate-wide cloud standards and policies and ensure compliance
Supporting Serialization and Asset digitization programs across
Responsible for the Continuous Integration and Deployment pipelines to support data science learning and production software delivery
Responsible for contributing to capability building of the Cloud Engineering team, including researching and staying up-to-date on best practices e.g. GitOps, IaC (infrastructure as code).