Senior Cloud Engineer, Computer Vision Infrastructure - Mississauga (Hybrid)

SENIOR CLOUD ENGINEER, COMPUTER VISION INFRASTRUCTURE

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).

Measures

• Successful roll out, development and continuous evolution and operation of cloud-based data science and machine learning platforms, both for research & development and for continuous operation
• Effective support of data science projects
• Reliability of systems
• Adoption of new systems and data science approaches

Authority / Decision Making

• Data science and machine learning frameworks: selection and implementation
• Tooling selection and implementation
• Working autonomously in a highly matrixed organization

Key Contacts

• Internal: Digital Data Sciences and Application Development team, Digital Product Teams (PM), Software Engineering Teams, Technology Services Cloud Engineering
• External: Potential vendors (e.g. for data science or monitoring tools)

Qualifications

• BS degree in Data Science, Computer Science, Engineering, Math, Statistics, Physics, or similar formal training or equivalent
• 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

Experience

• 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.

Skills and Knowledge

• 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

Compensation

The salary range for this position is $132,000 to $183,000 per year.

Additional compensation may include other monetary incentives or business benefits beyond medical and retirement benefits.