Dockerfile Data Validation Engineer

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. OpenTrain AI hires and contracts contributors for specialized projects that help shape how modern artificial intelligence is developed.

This contract gives experienced DevOps professionals the opportunity to apply their engineering skills to AI-focused data pipelines and build valuable experience in a rapidly growing field.

About AI Training and Data Infrastructure

AI training depends on reliable datasets, schemas, software environments, and evaluation workflows. Engineers help make this work dependable by building systems that check data and model artifacts before they reach downstream development or deployment processes.

This role focuses on the infrastructure and quality controls behind AI work, including containerized pipelines, data validation, versioning, and automated safeguards.

The Role

OpenTrain AI is seeking a Dockerfile Data Validation Engineer to build reliable quality controls for containerized data pipelines supporting advanced AI work. You will design, implement, and maintain validation workflows inside Docker-based build pipelines, ensuring that datasets, schemas, and model artifacts meet quality and compliance requirements before deployment.

• Contractor assignment with part-time options of 20, 30, or 40 hours per week
• Minimum commitment of 20 hours per week
• At least four hours of overlap with Pacific Time required
• Contract duration: 2–4 weeks
• Remote availability in India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, Mexico, and Brazil
• Compensation is not disclosed

What You’ll Do

You will create practical, automated checks that catch data and configuration issues early in containerized build and deployment workflows. The work combines Dockerfile development, validation scripting, CI/CD integration, and documentation.

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

Required Qualifications

This position requires strong hands-on experience with DevOps engineering and containerized build pipelines. The structured role information identifies the experience level as entry level, while the role requirements specifically call for at least four years of DevOps engineering experience; applicants should review that requirement carefully.

• At least four years of DevOps engineering experience
• Strong Docker and Dockerfile experience with containerized build pipelines
• Proficiency in Python or Bash for schema, integrity, and quality validation
• Experience integrating validation controls into CI/CD systems
• Knowledge of data formats, schemas, validation tools, and container registries
• English-language communication

Helpful Experience

The following experience is helpful for this assignment and can support work across AI-oriented data and infrastructure workflows.

• LLM research or evaluation projects
• MLOps workflows
• Data versioning
• Great Expectations
• Kubernetes
• Container security tools

Why Work With OpenTrain

OpenTrain helps people discover and grow careers in AI training and data labeling. Contributors can build a profile, show credible experience, find projects that match their skills, and develop a durable portfolio in a fast-growing technology field.

Creating an OpenTrain account is free, and remote project formats can make it easier to fit specialized AI work around your existing schedule.