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TensorGo Technologies

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3.6

based on 2 Reviews

14 TensorGo Technologies Jobs

ML Ops Engineer - TensorGo

TensorGo Technologies Private Limited

3.6

based on 2 Reviews

3-6 years

Hyderabad / Secunderabad, Bangalore / Bengaluru

1 vacancy

ML Ops Engineer - TensorGo

TensorGo Technologies

posted 4mon ago

Job Role Insights

Key skills for the job

Software Testing Python Cloud Computing Automation Testing Machine Learning Open Source

+ 3 more

Job Description

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, ML DevOps Engineer, or Data Engineer (or equivalent)
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

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