Вакансия Senior Application Platform Engineer / MLOps (Python/Terraform)

Skills

• Python
• Terraform
• Scala
• Ruby
• Bash
• GitHub Actions
• AWS SageMaker

Описание вакансии

We’re looking for a Senior Application Platform Engineer / MLOps to join our client, a leading American digital content platform with millions of users and a massive library of ebooks, audiobooks, and documents.
You’ll be working at the intersection of ML Engineering, Infrastructure, and Software Engineering to build scalable and efficient model deployment pipelines, internal tooling, and automation workflows.
This is a long-term, fully remote opportunity with a supportive international team and plenty of room for ownership.

About the project

Client is an American e-book and audiobook subscription service that includes one million titles. The platform hosts 60 million documents on its open publishing platform.
Core Platform provides robust and foundational software, increasing operational excellence to scale apps and data. As a Backend Engineer, your focus will be on building and optimizing scalable backend systems and data pipelines that support real-time processing and event-driven architectures.
Tech Stack: Terraform, Python, Ruby, Go, AWS Sagemaker, Apache Spark (Python/Scala), Jenkins Pipelines, Bash.

Must-have for the position

• 4–5+ years of experience as a software/infrastructure engineer
• Strong hands-on experience with Python and Terraform
• Solid understanding of algorithms and data structures
• Experience operating across ML, infrastructure, and software engineering disciplines
• Bachelor’s degree in Computer Science or equivalent practical experience.
• Strong communication skills and a collaborative mindset.
• Excellent English skills and the ability to engage with both technical and non-technical stakeholders.

Will be a strong plus

• Experience in Scala or Ruby (language polyglots are highly welcome)
• Understanding of how ML models are trained
• Scripting with Bash/Python
• GitHub Actions pipelines
• Experience with AWS Sagemaker
• Interest in trying to work with Go

Responsibilities

• Operate at the intersection of ML Engineering, Infrastructure Engineering, and Software Engineering
• Build and maintain model deployment and monitoring pipelines (AWS Sagemaker)
• Create and maintain client libraries used by internal ML teams
• Manage cloud infrastructure with Terraform
• Write production-ready scripts in Python, or Bash
• Provide guidance to ML engineers on model deployment and operations
• Maintain clean and clear internal documentation and runbooks