DevOps Engineer

Job Description

Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale Take offline models that the 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 Communicate with clients to build requirements and track progressSkills & Experience 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.) 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.) 5+ years of experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer Degree in Computer Science, Engineering or relevant field is preferred Key Skills: JD for DevOps Engineer:Mandatory Skills: Python, Cloud Services/Components (AWS), Workflow Management Tools, Deployment Platforms, Setting up CI/CD pipelines, AWS native tools, Configuring AWS network, security, and access control servicesRole & Responsibilities: Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale Take offline models that the 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 Communicate with clients to build requirements and track progressSkills & Experience 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.) 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.) 5+ years of experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer Degree in Computer Science, Engineering or relevant field is preferred Location: Bangalore, Chennai, Hyderabad, Mumbai, Pune Required Experience: 6-8 yrs,

Keyskills :
cloud computingsoftware testingcomputer sciencemachine learningmodel developmentawsturnlinuxcloudemailpython