MLOps Engineer

  • Irving, TX
  • Posted 5 hours ago | Updated 5 hours ago

Overview

On Site
$50+
Contract - W2

Skills

Architect and implement modern CI/CD pipelines to replace legacy build tools
with a focus on supporting Python-based AI/ML projects Develop and maintain Docker container build processes for creating
updating
and publishing base images Design and implement build stages for test coverage
coverage reporting
project packaging
and release management Integrate the new build system with existing software deployment processes for seamless AWS and On-prem deployment Collaborate with AI/ML teams to ensure the new infrastructure supports various AI/ML projects effectivelyRequired Skill Set: Extensive experience in DevOps practices
CI/CD pipelines
and containerization technologies (Docker
Kubernetes) Proficiency in Python and shell scripting
with a focus on build automation and deployment processes Strong knowledge of cloud platforms (particularly AWS) and infrastructure-as-code principles Familiarity with artifact repositories (e.g.
Artifactory) and version control systems (e.g.
Git) Understanding of AI/ML development workflows and the specific infrastructure requirements for deploying AI/ML models

Job Details

Position: MLOps Engineer Client - Verizon,

Location:Irving, TX - 2 days a week hybrid


Responsibilities:
Architect and implement modern CI/CD pipelines to replace legacy build tools, with a focus on supporting Python-based AI/ML projects
Develop and maintain Docker container build processes for creating, updating, and publishing base images
Design and implement build stages for test coverage, coverage reporting, project packaging, and release management
Integrate the new build system with existing software deployment processes for seamless AWS and On-prem deployment
Collaborate with AI/ML teams to ensure the new infrastructure supports various AI/ML projects effectively

Required Skill Set:
Extensive experience in DevOps practices, CI/CD pipelines, and containerization technologies (Docker, Kubernetes)
Proficiency in Python and shell scripting, with a focus on build automation and deployment processes
Strong knowledge of cloud platforms (particularly AWS) and infrastructure-as-code principles
Familiarity with artifact repositories (e.g., Artifactory) and version control systems (e.g., Git)
Understanding of AI/ML development workflows and the specific infrastructure requirements for deploying AI/ML models

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