MLOPS Engineer - Cogniphi

Join our team and help build the machine learning production infrastructure for our clients. As an experienced server-side developer, you will design data pipelines, develop and deploy scalable tools and services, and evaluate new technologies to improve machine learning systems performance, maintainability, and reliability. Responsibilities: Designing data pipelines and engineering infrastructure to support clients enterprise machine learning systems at scale. Taking offline models from data scientists and turning them into a real machine learning production system. Developing and deploying scalable tools and services for clients to handle machine learning training and inference. Identifying and evaluating new technologies to improve the performance, maintainability, and reliability of clients machine learning systems. Applying software engineering rigor and best practices to machine learning, including CI/CD, automation, etc Supporting model development, with an emphasis on auditability, versioning, and data security. Facilitating the development and deployment of proof-of-concept machine learning systems. Communicating with clients to build requirements and track progress. Requirements: Over 3 years of experience building production-quality software. Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent). Strong software engineering skills in complex, multi-language systems. Fluency in Python. Comfort 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 Kubeflow is a must and the ability to understand each framework and select the best tool for need is a must). 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. Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc). What you will be part of Niche projects in Computer Vision, AI, and Telematics in multiple industry sectors Platform and product implementations Tier -1 technology partners and supportive management ensure individual as we'll as overall company growth Global Work Culture Psychological Safe Workplace