updated 7 days ago
Software Engineer (MLOps)
Vacancy from Hirify Global, list of international tech companiesPlus is required to make matches and apply
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Job description
Text:
TL;DR
Software Engineer (MLOps) (Machine Learning/Gaming): Building reliable and scalable systems for player trust and safety, including application services, APIs, data processing pipelines, and cloud infrastructure with an accent on production machine learning deployment. Focus on operating large-scale pipelines, deploying predictive models, and improving platform quality and reliability across end-to-end projects.
Location: Office-based in Montreal, Canada; Chengdu, China; Bordeaux, France; or Stockholm, Sweden
Company
is a global gaming company creating interactive entertainment experiences, including Assassin’s Creed, Rainbow Six, and Just Dance.
What you will do
- Lead end-to-end software and machine learning operations projects from design through production use.
- Design, develop, and maintain application services and APIs for data and model sharing.
- Build and operate large-scale data processing pipelines.
- Deploy and manage scalable cloud infrastructures using modern containerization and monitoring practices.
- Improve platform quality, reliability, and scalability.
- Collaborate with data and machine learning specialists to bring predictive models into production.
Requirements
- Strong software development or data engineering skills using Python or Rust.
- Experience designing and consuming web service APIs.
- Practical knowledge of cloud environments and containerized systems, including Kubernetes, ArgoCD, AWS, and Terraform.
- Working knowledge of machine learning and experience deploying predictive models into production.
- Ability to connect high-level vision with technical details and communicate clearly in collaborative environments.
- Familiarity with large-scale data processing tools or platform operations.
Culture & Benefits
- Work within the Player Data domain of ’s Data Office.
- Focus on supporting players in a safe and respectful environment.
- Collaborate with data and machine learning specialists on applied research and production engineering.
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