Senior Machine Learning Engineer

• You will 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
• You will collaborate with data scientists and engineers to translate business needs into effective and efficient ML systems and applications
• You will own the development and maintenance of core functionalities within ML applications, including ML pipelines, model training and deployment, and monitoring and evaluation
• You will drive the functional stream of work by providing technical expertise, handling team discussions and ensuring timely delivery of assigned tasks
• You will stay ahead of the curve by actively exploring and implementing the latest tools, frameworks and offerings in the ML landscape
• You will facilitate collaborative problem solving within the team by actively listening, communicating effectively and mentoring other engineers
• You will contribute to the development and execution of the team's overall ML strategy, aligning technical capabilities with business objectives
• You will proactively identify and address challenges related to ML systems and applications, proposing solutions and implementing improvements

• An advanced English level is required
• You must have a strong experience with LLM and AI
• You have experience in writing clean, maintainable and testable code, demonstrating attention to refactoring and readability of the code
• You are proficient in scripting languages such as Python or Shell for automation and task streamlining
• You have knowledge of distributed systems and scalable architectures to handle large-scale ML applications
• You have experience with building, deploying, and maintaining ML systems using relevant ML techniques and platforms, i.e.: Scikit-learn, Tensorflow, MLFlow, Kubeflow, Pytorch
• You have experience with building, deploying and maintaining ML systems and experience with application of MLOps principles and CI/CD to ML
• You have experience in machine learning engineering and data science, are familiar with key ML concepts, algorithms and frameworks, and understand ML model lifecycles
• You have 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
• You have 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
• You understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust along the way
• You are resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives
• You don’t shy away from risks or conflicts, instead you take them on and skillfully manage them
• You are eager to coach, mentor and motivate others and you aspire to influence teammates to take positive action and accountability for their work
• You enjoy influencing others and always advocate for technical excellence while being open to change when needed

Learning & Development: There is no one-size-fits-all career path at Thoughtworks: however you want to develop your career is entirely up to you. But we also balance autonomy with the strength of our cultivation culture. This means your career is supported by interactive tools, numerous development programs and teammates who want to help you grow. We see value in helping each other be our best and that extends to empowering our employees in their career journeys