Machine learning Devops Engineer

Key Responsibilities :
Develop, deploy, and optimize machine learning models to address business challenges and opportunities.
Design and implement robust machine learning infrastructure on AWS, ensuring scalability and performance.
Utilize Databricks for large-scale data processing and machine learning tasks.
Work with Airflow on EKS, Databricks workflows for orchestrating and automating workflows.
Ensure seamless integration and deployment using GitLab/GitHub for CI/CD.
Perform infrastructure development using Infrastructure as Code (IaC) tools such as Terraform and CloudFormation and the likes.
Act as a consultant on best practices, guiding internal teams on ML best practices, DevOps, and AWS tools.
Keep updated with the latest machine learning trends, tools, and methodologies to drive innovation.
Qualifications :
2+ years experience in a machine learning engineering function as a ML engineer, DevOps engineer, software engineer, or similar disciplines diligently applying principles, practices, and theory for team deliverables
Comfortable operating in an SDLC environment and deploying production engineering models and code
Strong expertise with AWS DevOps tools and methodologies, including Infrastructure as Code using Terraform, CloudFormation, or the likes.
Hands-on experience with Databricks/Spark, and/or Airflow.
Proficient in CI/CD practices using GitLab/Github, Kubernetes
Familiarity with developing machine learning models; experience in data engineering will be advantageous.
AWS certification is a plus.
Excellent communication and collaboration skills (English)
Strong problem-solving skills and a keen analytical mind.

Globant is an EOE M/F/D/V. For many positions, relocation is available if needed. Globant does not accept unsolicited third party resume

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