{"id":2703369,"url":"https://alion.io/job/thoughtworks-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":6249,"name":"Thoughtworks","domain":"thoughtworks.com","url":"https://alion.io/company/thoughtworks","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":88,"open_postings":14,"ghost_share":0,"stale_share":0.429,"repost_share":0.071,"time_to_fill_p50_days":36,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":53000,"max_usd":113000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-16T08:28:47Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T07:57:48Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"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.\nAs 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.\nDue to the nature of the projects and specific client security clearance, this role requires the successful candidate to be a Singapore Citizen or PR of Singapore.\nJob responsibilities\nYou 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\nYou will collaborate with data scientists and engineers to translate business needs into effective and efficient ML systems and applications\nYou will own the development and maintenance of core functionalities within ML applications, including ML pipelines, model training and deployment, and monitoring and evaluation\nYou will drive the functional stream of work by providing technical expertise, handling team discussions and ensuring timely delivery of assigned tasks\nYou will stay ahead of the curve by actively exploring and implementing the latest tools, frameworks and offerings in the ML landscape\nYou will facilitate collaborative problem solving within the team by actively listening, communicating effectively and mentoring other engineers\nYou will contribute to the development and execution of the team's overall ML strategy, aligning technical capabilities with business objectives\nYou will proactively identify and address challenges related to ML systems and applications, proposing solutions and implementing improvements\nJob qualifications\nTechnical Skills\nYou have experience in writing clean, maintainable and testable code, demonstrating attention to refactoring and readability of the code\nYou are proficient in scripting languages such as Python or Shell for automation and task streamlining\nYou have knowledge of distributed systems and scalable architectures to handle large-scale ML applications\nYou have experience with building, deploying, and maintaining ML systems using relevant ML techniques and platforms, i.e.: Scikit-learn, Tensorflow, MLFlow, Kubeflow, Pytorch\nYou have experience with building, deploying and maintaining ML systems and experience with application of MLOps principles and CI/CD to ML\nYou have experience in machine learning engineering and data science, are familiar with key ML concepts, algorithms and frameworks, and understand ML model lifecycles\nYou 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.\nYou 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\nProfessional Skills\nYou 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\nYou are resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives\nYou don’t shy away from risks or conflicts, instead you take them on and skillfully manage them\nYou are eager to coach, mentor and motivate others and you aspire to influence teammates to take positive action and accountability for their work\nYou enjoy influencing others and always advocate for technical excellence while being open to change when needed\nOther things to know\nLearning & Development\nThere 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.\nResponsible Use of AI in Recruitment\nAt Thoughtworks, we use AI tools to support our recruitment team with administrative tasks such as drafting communications, scheduling interviews and writing job descriptions.\nCrucially, our AI tools do not screen, assess, rank or make hiring decisions. Every application is reviewed by our team and all selection decisions are made exclusively by our interviewers and hiring managers.\nWe are committed to fairness and responsible AI. We actively manage our AI systems by testing, monitoring for biased outcomes and implementing mitigation measures. We hold our third-party vendors to these same high standards through a rigorous governance process. For additional information, please see our full Thoughtworks AI Policy for Recruitment.\nAbout Thoughtworks\nThoughtworks is a dynamic and inclusive community of bright and supportive colleagues who are revolutionizing tech. As a leading technology consultancy, we’re pushing boundaries through our purposeful and impactful work. For 30+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator. Bring your brilliant expertise and commitment for continuous learning to Thoughtworks. 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