Remote Senior Machine Learning Engineer

Summary

Join Thoughtworks and thrive as a Senior Machine Learning Engineer, building, maintaining, and testing machine learning applications architecture and infrastructure. Contribute to design and drive development of robust scalable architectures and infrastructure for deploying and managing machine learning applications.

Requirements

• Advanced/fluent English for daily conversation
• Knowledge with Azure cloud
• Experience in writing clean, maintainable and testable code, demonstrating attention to refactoring and readability of the code
• Proficient in scripting languages such as Python or Shell for automation and task streamlining
• Knowledge of distributed systems and scalable architectures to handle large-scale ML applications
• Experience with building, deploying, and maintaining ML systems using relevant ML techniques and platforms, i.e.: Scikit-learn, Tensorflow, MLFlow, Kubeflow, Pytorch
• Experience with building, deploying and maintaining ML systems and experience with application of MLOps principles and CI/CD to ML
• Experience in machine learning engineering and data science, are familiar with key ML concepts, algorithms and frameworks, and understand ML model lifecycles
• Experience with designing and operating the infrastructure required to run different types of ML training and serving workloads, i.e.: on-premise vs. cloud infrastructure, infrastructure as code, monitoring, etc
• Hands-on experience with on-premise and cloud services for building and deploying ML pipelines, i.e.: Azure, AWS, GCP or Databricks and associated ML managed services

Responsibilities

• Contribute to design and drive the development of robust scalable architectures and infrastructure for deploying and managing machine learning (ML) applications, ensuring high availability, performance and security
• Collaborate with data scientists and engineers to translate business needs into effective and efficient ML systems and applications
• Own the development and maintenance of core functionalities within ML applications, including ML pipelines, model training and deployment, and monitoring and evaluation
• Drive the functional stream of work by providing technical expertise, handling team discussions and ensuring timely delivery of assigned tasks
• Stay ahead of the curve by actively exploring and implementing the latest tools, frameworks and offerings in the ML landscape
• Facilitate collaborative problem solving within the team by actively listening, communicating effectively and mentoring other engineers
• Contribute to the development and execution of the team's overall ML strategy, aligning technical capabilities with business objectives
• Proactively identify and address challenges related to ML systems and applications, proposing solutions and implementing improvements

Benefits

Learning & Development: interactive tools, numerous development programs and teammates who want to help you grow