Dockerfile Data Validation Engineer

You will build and maintain validation workflows inside Docker-based build pipelines used to support AI systems. Your work will help check datasets, schemas, and model artifacts before deployment.

The role combines container configuration, metadata standards, scripting, and automated quality checks. You will work across data engineering, machine learning, and DevOps workflows.

• Develop and improve Dockerfiles with built-in data-validation steps.
• Add Docker LABEL metadata for dataset versions, schemas, and data lineage.
• Write Python or Bash scripts for schema checks, data integrity checks, and quality control.
• Integrate validation into CI/CD pipelines and make builds fail when data does not pass checks.
• Document Dockerfile labels, validation logic, and data-governance standards.
• Support reliable, reproducible, and fully validated containerized data pipelines.

What it pays and takes

This is a part-time contractor role. The listing does not specify a pay rate or fixed payment.

The role is marked entry level, but the requirements include at least four years of DevOps experience. English is required, and applicants must be based in one of the listed countries.

• Hours: 20+ hours per week.
• Work type: Part-time contract.
• Location: India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Türkiye, Mexico, or Brazil.
• Language: English.
• Required: At least four years of DevOps experience and strong Docker and Dockerfile experience.
• Required: Python or Bash scripting skills for validation work.
• Required: Knowledge of data formats, schemas, and validation tools.
• Required: Familiarity with CI/CD systems and container registries.
• Helpful: LLM research or evaluation, developer tools, automation agents, MLOps, data versioning, Great Expectations, Kubernetes, or container security.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work is the human work behind systems that learn from labeled data, reviewed outputs, and carefully checked technical examples. Engineers and other specialists help make these datasets and workflows accurate, consistent, and ready for model development.