DevOps / MLOps Engineer

Job Description

Profile : We are looking for an experienced and high-energy ML Ops Engineer. The primary function of this role is to design enterprise architecture. Envision and drive solution architecture after hearing the product s vision and user stories with ability to envision and drive a proactive architectural roadmap for an existing product keeping in mind the future requirements. Requirements : - Experience building end-to-end systems as a Platform Engineer, MLOps Engineer, or Data Engineer (or equivalent). - Hands-on expertise in Python and ML frameworks. - Expertise with Linux administration. - Experience working with cloud computing and database systems. - Experience building custom integrations between cloud-based systems using APIs. - Experience developing and maintaining ML systems built with open source tools. - Experience developing with containers and Kubernetes in cloud computing environments. - Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.). - Ability to translate business needs to technical requirements. - Strong understanding of software testing, benchmarking, and continuous integration. - Exposure to machine learning methodology and best practices. - Experience with Prometheus and Grafana integrations for highly scalable environments. Responsibilities : - Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale. - Take offline models data scientists build and turn them into a real machine learning production system. - Develop and deploy scalable tools and services to handle machine learning training and inference. - Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems. - Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc. - Support model development, with an emphasis on auditability, versioning, and data security. - Facilitate the development and deployment of proof-of-concept machine learning systems.