**About Chiper** Chiper is a technology startup present in Colombia, Mexico and Brazil. The platform offers mass consumption products at direct prices from manufacturers to independent businesses such as neighborhood stores, liquor stores, mini markets, etc. Our purpose is to create bridges between middle and low income people who are looking for better alternatives to buy their essential goods, their needs and opportunities of traditional and obsolete models with technology, always thinking about that end user and their alternatives. Our inspiration is the final buyer that is supplied in these independent businesses. Chiper was born as a project in 2018 and in 2019 was launched as a company. We have had an accelerated growth and we seek to add to our team entrepreneurs who dream of eating the world, who enjoy an environment of uncertainty and co-creation. We want people who first and foremost want to be the best at what they do and also help their peers to be the best. **Your principal responsibilities will be**: As a MLOps Engineer, you will Unify ML project development and ML tool implementation(ops) to standardize and optimize continuous delivery of high-performance models into production. **What You’ll Do**: - Lead initiatives aimed at making Chiper's machine learning engineers and data scientists more productive. - Make improvements and extend the underlying infrastructure that powers machine learning teams. - Simplify the development and implementation cycles of Machine Learning models. - Establish best practices for Machine Learning pipelines. - Design and build effective and easy-to-use infrastructure, tools, and automation to accelerate the life cycle of machine learning models. - Collaborate with machine learning engineers and product managers to develop tools that support model experimentation, training, and deployment operations. **Basic Qualifications**: - Programming languages (Python (preferred) or other languages such as Java, C#, Golang, etc.). - Experience with ML infrastructure and ML DevOps - Knowledge in development and implementation of distributed systems and infrastructure-as-code - Experience working with ML engineers to support the entire ML engineering lifecycle, from experimentation to production operations. - Knowledge in Docker and Kubernetes - Knowledge of Kubernetes and ML CI/CD workflows - Proven experience with GCP, AWS, or Azure - Management of Scrum and different agile methodologies. - Analytical and problem-solving oriented. **Preferred Qualifications**: - At least 1 year of experience in ML and model deployment in production (specialized experience in the area can be gained through education and full-time work experience, additional training, coursework, research, or similar) - PERSONAL DATA PROTECTION POLICY. THE APPLICANT expressly declares to know and agree with the Data Handling Authorization Policy, which was duly shown and informed, and which is in force in the Company and applicable to any person who voluntarily delivers their personal data and / or sensitive to CHIPER SAS In addition, THE APPLICANT expressly declares to know the web location of the CHIPER SAS Data Management Authorization Policy referred to here, and which in any case can be consulted for the _Personal Data Protection Policy for Colombia_ and for the _Personal Data Protection Policy for Mexico_