ML Ops Engineer

2 weeks ago

Hyderabad, Telangana, India Tensorgo Technologies Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Profile

We are looking for an experienced and high-energy ML Ops Engineer. The primary function of this role is to design enterprisearchitecture. Envision and drive solution architecture after hearing the product svision and user stories with ability toenvision and drive a proactive architectural roadmap for anexisting 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.

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