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Senior Machine Learning Engineers at Thoughtworks build, maintain and test the architecture and infrastructure for managing machine learning applications. They are involved in supporting and contributing to the design of the end-to-end applications and products. They are responsible for building core capabilities including technical and functional machine learning systems and applications, being the anchor for functional streams of work and are accountable for timely delivery. As a senior machine learning engineer, you will work on the latest tools, frameworks and offerings while also being involved in enabling credible and collaborative problem solving to execute on a strategy.
Job Responsibility:
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
Requirements:
An advanced English level is required
Strong experience with LLM and AI
Experience in writing clean, maintainable and testable code
Proficient in scripting languages such as Python or Shell
Knowledge of distributed systems and scalable architectures
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)
Understand the importance of stakeholder management
Resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives
Don’t shy away from risks or conflicts, instead you take them on and skillfully manage them
Eager to coach, mentor and motivate others and you aspire to influence teammates to take positive action and accountability for their work
Enjoy influencing others and always advocate for technical excellence while being open to change when needed