TalentAQ

TalentAQ

AI / MLOps Engineer

EngineeringFull Time10 years

Required Skills
2 skills

AI
MLOps

Job Description

<h3>Job Overview</h3><p>We are looking for an experienced AI / MLOps Engineer to join our innovative team. As an AI / MLOps Engineer, you will be responsible for developing, deploying, and maintaining machine learning models. You will work closely with data scientists and engineers to ensure the reliability and scalability of our AI solutions.</p><h3>Key Responsibilities</h3><ul><li>Develop and deploy machine learning models using various AI frameworks.</li><li>Automate the deployment and monitoring of AI models.</li><li>Collaborate with data scientists to optimize model performance.</li><li>Implement CI/CD pipelines for machine learning workflows.</li><li>Troubleshoot and resolve issues related to AI model deployment.</li></ul><h3>Required Skills</h3><ul><li>Proficiency in Python and machine learning libraries such as TensorFlow or PyTorch.</li><li>Experience with DevOps tools such as Docker and Kubernetes.</li><li>Strong understanding of cloud platforms like AWS or Azure.</li><li>Familiarity with MLOps best practices.</li></ul>

Job Overview

We are looking for an experienced AI / MLOps Engineer to join our innovative team. As an AI / MLOps Engineer, you will be responsible for developing, deploying, and maintaining machine learning models. You will work closely with data scientists and engineers to ensure the reliability and scalability of our AI solutions.

Key Responsibilities

  • Develop and deploy machine learning models using various AI frameworks.
  • Automate the deployment and monitoring of AI models.
  • Collaborate with data scientists to optimize model performance.
  • Implement CI/CD pipelines for machine learning workflows.
  • Troubleshoot and resolve issues related to AI model deployment.

Required Skills

  • Proficiency in Python and machine learning libraries such as TensorFlow or PyTorch.
  • Experience with DevOps tools such as Docker and Kubernetes.
  • Strong understanding of cloud platforms like AWS or Azure.
  • Familiarity with MLOps best practices.

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