At CloudFactory, we are a mission-driven teampassionate about unlocking the disruptive potential of AI for theworld. By combining advanced technology with a global network oftalented experts, we make unusable data usable and inferencereliable and trustworthy, driving real-world business value atscale. More than just a workplace, we’re a global community foundedon strong relationships and the belief that meaningful worktransforms lives. Our commitment to earning, learning, and servingfuels everything we do, as we strive to connect one million peopleto meaningful work and build leaders worth following. Our CultureAt CloudFactory, we believe in building a workplace where everyonefeels empowered, valued, and inspired to bring their authenticselves to work. We are: - Mission-Driven: We focus on creatingeconomic and social impact. - People-Centric: We care deeply aboutour team’s growth, well-being, and sense of belonging. -Innovative: We embrace change and find better ways to do things,together. - Globally Connected: We foster collaboration betweendiverse cultures and perspectives. If you’re ready to earn, learn,serve, and be part of a vibrant global community, CloudFactory isyour place! About the role: We are seeking a Junior ML Ops Engineerto join our team and help us provide a service designed to ensureML models remain operational in production environments. Theprimary objective is to maintain the continuous performance ofthese models with minimal disruption to the client’s businessgoals, specifically focused on hiring and retaining transportationdrivers. As customers expand their ML models, this service willscale to meet their needs. Our approach combines proprietarytechnology with expert talent to deliver a best-in-class platformfor machine learning, paired with scalable processes,configuration, and systems. A Complete Machine Learning Platform -Submit and deploy models - data scientists can easily upload theirtrained models and leverage the platform’s automated pipelines toseamlessly transition them into production environments. - Monitorand optimize performance - the platform provides real-timemonitoring dashboards and performance metrics, enabling users totrack the accuracy, efficiency and overall effectiveness ofdeployed models. - Collaborate and iterate - features A/B testingand version control to facilitate collaboration and continuousimprovement of models, allowing users to refine their models andmaximize their impact on business objectives. What You’ll Do: -Monitor the health, pipelines and alerting of machine learningmodels deployed on customer’s infrastructure. - Respond or alertcustomers when there has been an outage or issue with one of theirmodels. - Incident Management and Priority Classification to makesure the right support team is available to solve the problem, ifyou can’t solve it yourself. - Build quarterly business reviews toprovide updates on the health of the ML Models. - Evaluatechampion/challenger models to see if a new model should bepromoted. - Ensure there is no bias introduced into the models asnew champion models are introduced or additional data is loaded.Requirements - Background in computer science, informatics, orrelated fields - Passion for Machine Learning and AI: An eagerlearner who is excited about working with cutting-edge MLtechnologies and is passionate about optimizing and maintaining MLmodels in production environments. - Early Career in MLOps or MLEngineering: Ideally, you’re an aspiring Data Scientist or JuniorML Engineer with a strong desire to grow in the field of MLOps andAI operations. - Technical Skills: - Proficiency in Python fordeveloping and automating ML workflows. - Familiarity with Azurefor cloud-based ML services. - Experience with MLFlow for modeltracking and management. - Comfort with PowerBI and Grafana formonitoring and visualizing model performance. - Experience withDatabricks for data engineering and collaborative ML workspaces. -Git: Solid understanding of version control, particularly incollaborative development environments. - Interest inContainerization: While not required, experience with Kubernetesand containerized applications is a plus, as some of our workflowsmay involve containerization. - A Collaborative Mindset: You thrivein a team setting and are ready to contribute to model improvement,A/B testing, and iterative development. - Attention to Detail: Afocus on model performance, bias prevention, and ensuring optimalmodel behavior as new data and models are introduced. AdditionalInformation This role provides MLOps coverage from 11am to 6pmColombia time for a US-based customer. You will be required to workduring these hours and potentially outside of them if a model hasissues. This is a 6-month collaboration position. We are onlyaccepting resumes and applications in English.