This role involves creating and managing Dockerfile labels, metadata standards, and validation scripts that ensure datasets, schemas, and model artifacts meet quality and compliance requirements before deployment.

You will work closely with data engineering, machine learning, and DevOps teams to build reliable, reproducible, and fully validated containerized data pipelines.

Perks of Freelancing With Turing:

• Work in a fully remote environment.
• Opportunity to work on cutting-edge AI projects with leading LLM companies.

Employment type  : Contractor assignment (no medical/paid leave).

Duration of contract : 2-4 weeks; [expected start date is next week].

• Develop and optimize Dockerfiles with built-in data-validation steps.
• Implement LABEL metadata for dataset versions, schemas, and lineage.
• Create validation scripts (Python/Bash) for schema checks, data integrity, and quality control.
• Integrate validation steps into CI/CD pipelines and enforce fail-on-bad-data checks.
• Document standards for Dockerfile labeling, validation logic, and data governance.

• Experienced DevOps engineers with 4+ years of experience.

Evaluation Process (approximately 75 mins) :

• Interviews (30-60 min technical discussion in QODE).

Work arrangement: Fully remote

Applicant eligibility: 10 eligible countries