Job Opportunities API

About Entrupy   Entrupy is a global technology company whose mission is to protect businesses, borders and consumers from transacting in counterfeit goods. Entrupy has developed a patented technology system which utilizes a combination of AI and computer vision to instantly identify and authenticate high value physical goods.   Entrupy’s solutions serve business customers including leading luxury brands, retailers, e-commerce marketplaces and online resellers in over 60 countries. Entrupy is growing quickly with team members based in the US, India, Japan and Brazil. Entrupy’s solutions in market: ●   Entrupy Apparel Authentication ●   Entrupy Bags & Leather Goods Authentication ●  Entrupy Sneaker Authentication ●  Entrupy Fingerprinting   As we continue to build... We’re seeking curious, growth minded thinkers to help shape our vision, structures and systems; playing a key-role as we launch into our ambitious future. If you’re invigorated by our mission, values, and drive to change the world — we’d love to have you apply. About the role Entrupy is seeking a  ML-Ops Engineer to join our growing team supporting machine learning infrastructure and operations. This is a great opportunity for someone early in their ML-Ops or data engineering career who is excited to work on real-world AI products and wants to grow their experience in deploying and managing machine learning systems in production. As part of this role, you'll collaborate with experienced engineers, data scientists, and product teams to help streamline ML workflows—from model training and evaluation to deployment and monitoring. Location: Bangalore, India (Hybrid) Reports To: VP of Engineering This role involves a mix of technical contribution, and operations work. In addition, this role will serve as a point of contact with various US-based and IN-based teams responsible for model delivery: annotation teams, infrastructure engineers, machine learning engineers, and products. Some project areas this team is responsible for include: Infrastructure and libraries to define, deploy, run, and monitor training and inference jobs Providing interfaces and tooling for ML engineers to work with Job graph visualization and analytics Bringing research models and code to production Hybrid cloud server provisioning and automation Internal dashboards and annotation tools Contributing to best practices and methodology guidelines for data science teams Platform advocacy, training, and mentoring What You'll Do: Collaborate with engineering leads to define and implement new features and subsystems for our machine learning platform. Build interfaces and tools for ML researchers, engineers, and data teams. Operationalize research models — bringing them from prototype to production and scaling them effectively. Design, build, and maintain data and training pipelines, job orchestration systems, and monitoring setups. Develop and maintain automated testing and contribute to integration testing and rollouts. Assist research and product teams in the use of the platform. Stay in regular communication with ML research and product teams to understand how the platform can best assist in other teams' objectives. Collaborate with infrastructure teams for provisioning, deployment workflows, and automation. Who you are: 3-5 years of experience working in ML-Ops, data engineering, backend development, or DevOps. Exposure to deploying or maintaining ML models in real-world applications (internships or full-time roles). Experience writing Python scripts and familiarity with software engineering best practices (e.g., version control, testing). Comfortable working with cloud platforms (AWS preferred) or containerized environments like Docker. Curious, proactive, and eager to learn from more experienced team members. Goo