New opportunity

EverestDX - AI Testing Engineer - Media Generation

Everestdx · Hyderabad, India

About this role

Everestdx seeks an AI Testing Engineer for media generation and enhancement solutions in Hyderabad or Bangalore. The full-time role requires 3 to 5 years of experience in software testing, quality engineering, or AI testing. The engineer will work with AI engineers, product teams, and data scientists to assess whether generated content meets enterprise quality, security, and Responsible AI standards.

Responsibilities

Validate generated images for visual quality, consistency, prompt accuracy, sharpness, resolution, color accuracy, texture preservation, artifacts, and realism. Perform functional and regression testing of image generation and enhancement models.
Test capabilities including upscaling, restoration, Generative Fill, image editing, background replacement, and image expansion. Compare model outputs and benchmark them against quality, consistency, and business requirements.
Check generated images for copyright, licensing, intellectual property, and Responsible AI compliance, including copyrighted logos, trademarks, branded assets, and other potential risks.
Test image generation and enhancement APIs; create and run automated AI-workflow tests using Python and testing frameworks. Document defects, prepare test reports, support model validation and release certification, and contribute to testing standards and frameworks.

Requirements

Experience testing AI-powered applications or Generative AI solutions; understanding of image generation and enhancement workflows, image quality validation, visual inspection, model evaluation, and prompt-based testing.
Hands-on experience with at least one of Adobe Firefly, Magnific AI, OpenAI Image APIs, Stable Diffusion, FLUX, Midjourney, or Ideogram. Knowledge of copyright, image licensing, Responsible AI, and AI governance principles.
REST API testing experience with Postman or similar tools; Python proficiency for test automation; experience with PyTest, Playwright, Selenium, or Robot Framework; familiarity with Git, Azure DevOps, or GitHub. Strong analytical, debugging, and documentation skills.
A bachelor's or master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.

Preferred

Experience with computer vision or Generative AI applications; knowledge of OpenCV, Pillow, or other image-processing libraries; understanding of image classification and model benchmarking; experience with Microsoft Azure or AWS; and exposure to MLOps, CI/CD pipelines, and AI model lifecycle management.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI testingGenerative AIImage generationImage enhancementImage quality validationVisual inspectionAI model evaluationModel benchmarkingPrompt-based testingFunctional testingRegression testingREST API testingTest automationPythonPyTestPlaywrightSeleniumRobot FrameworkPostmanAdobe FireflyMagnific AIOpenAI Image APIsStable DiffusionFLUXMidjourneyIdeogramCopyright complianceImage licensingResponsible AIAI governance principles‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Image quality and prompt fidelity
Validate generated images for visual consistency and prompt accuracy, including sharpness, resolution, color accuracy, texture preservation, artifacts, and realism.
Generation and enhancement coverage
Perform functional and regression testing across image generation, upscaling, restoration, Generative Fill, editing, background replacement, and expansion.
Model comparison against business needs
Compare outputs from multiple AI models and benchmark quality and consistency against business requirements.
Copyright and Responsible AI review
Check generated images for licensing, intellectual-property, and Responsible AI risks, including logos, trademarks, and branded assets.
Automated testing and release evidence
Test image-service APIs, automate AI-workflow tests with Python and testing frameworks, document defects and reports, and participate in model validation and release certification.

Likely challenges to think through

Making visual judgments repeatable
Preparation hypothesis: generated images may vary while qualities such as realism are partly subjective, making regressions hard to distinguish from acceptable variation. Prepare a sample evaluation rubric with prompt-specific checks, reference outputs, and clear defect evidence.
Comparing models fairly
Preparation hypothesis: models and enhancement features may have different strengths, so a single quality score could obscure business-relevant trade-offs. Prepare an approach using the same prompts or source images where applicable, separate quality dimensions, and documented comparison criteria.
Handling uncertain rights risks
Preparation hypothesis: a visual review may flag a possible branded asset without establishing its licensing status. Prepare to explain how to record the image, prompt, and suspected issue, distinguish a risk flag from a legal conclusion, and seek appropriate review.

Practice your answers

Technical practice

How would you assess the visual quality of an AI-generated image?

What to coverSuggested preparation: Describe a repeatable inspection rubric covering sharpness, resolution, color accuracy, textures, artifacts, realism, and consistency. Explain when visual review is more useful than an automated image measure, and how you would record findings.

How would you test whether an image accurately follows a prompt?

What to coverSuggested preparation: Separate required subjects, attributes, composition, and exclusions into observable checks. Explain how you would assess repeated outputs without treating normal generation variability as a defect.

How would you validate an image enhancement feature?

What to coverSuggested preparation: Choose a posted capability, such as upscaling, restoration, Generative Fill, or background replacement, and describe input images and expected changes. Address preservation of unaffected regions alongside improvements in detail and visual quality.

How would you build a functional and regression suite for image generation?

What to coverSuggested preparation: Cover supported operations, input handling, and previously observed failures. Explain how you would use stable checks where possible and review variable outputs against defined quality criteria rather than demanding identical images.

How would you benchmark outputs from two image models?

What to coverSuggested preparation: Compare them on the same prompts and evaluation criteria, including quality, consistency, and stated business requirements. Explain how you would document trade-offs when one model performs better on some criteria but worse on others.

How would you test generated images for copyright and Responsible AI risks?

What to coverSuggested preparation: Discuss checks for recognizable logos, trademarks, branded assets, and licensing concerns, alongside applicable Responsible AI criteria. Explain how you would document a suspected issue and seek appropriate review rather than claim that visual inspection proves legal compliance.

What would you test in an image generation or enhancement REST API?

What to coverSuggested preparation: Discuss valid and invalid requests, response structure, returned image properties, and error handling using Postman or a similar tool. Connect API responses to image-quality checks so that a successful request is not mistaken for a good output.

How would you automate AI-workflow tests with Python?

What to coverSuggested preparation: Outline reusable test data, API calls, assertions, and reporting with Python and a listed testing framework such as PyTest. Explain which checks can be automated reliably and which still need visual inspection.

How would you investigate an intermittent image-quality defect?

What to coverSuggested preparation: Describe how you would capture the prompt, input image if any, model or platform, settings, output, and repetition frequency. Explain how you would distinguish a reproducible regression from expected output variation and write a useful defect report.

What evidence would you provide for model validation and release certification?

What to coverSuggested preparation: Organize functional and regression results, quality comparisons, unresolved defects, and copyright or Responsible AI concerns against agreed criteria. Explain how you would communicate limitations clearly without implying that testing guarantees risk-free outputs.

Behavioural practice

Tell me about a time you resolved a quality disagreement across teams.

What to coverSuggested preparation: Use a real situation involving differing views of an output or acceptance criterion, then explain your actions and the result. Focus on how you used evidence and collaborated with engineering, product, or another relevant team.

Describe a difficult defect you investigated.

What to coverSuggested preparation: Give the situation, the steps you took to reproduce and isolate the issue, and the result. Highlight your debugging and documentation choices rather than presenting an unverified cause as fact.

Tell me about a time you raised a content, compliance, or security concern.

What to coverSuggested preparation: If you have a relevant real example, explain the situation, what you observed, how you communicated or escalated it, and the result. Be clear about the limits of your own assessment and any review needed from others.

Describe a time you improved a testing standard or framework.

What to coverSuggested preparation: Explain the gap you noticed, the change you made or proposed, and its observable result. Relate the example to repeatability, coverage, or clearer reporting where your experience supports it.

Tell me about a time you had to balance competing quality priorities.

What to coverSuggested preparation: Use a real situation to explain the competing criteria, your actions to gather evidence or clarify priorities, and the result. Show how you communicated the trade-off without claiming that every concern was fully resolved.

Sharpen your knowledge

Topics to prepare

Image quality criteria
Posting: Images are assessed for sharpness, resolution, color accuracy, texture preservation, artifacts, realism, consistency, and prompt accuracy. Revision exercise: define what a pass, failure, and borderline result would look like for each criterion on a small set of sample images.
Prompt-based evaluation and model benchmarking
Posting: The role compares outputs across models against quality, consistency, and business requirements. Revision exercise: design a comparison rubric for several outputs from the same prompt, including how to record variation without treating every difference as a defect.
Enhancement workflows and regression scope
Posting: Coverage includes upscaling, restoration, Generative Fill, editing, background replacement, and image expansion. Revision exercise: map the expected input, output, and likely failure modes for each capability, then identify cases worth rerunning after a model change.
Copyright and Responsible AI review
Posting: Generated images must be checked for licensing, intellectual-property, and Responsible AI risks, including logos, trademarks, and branded assets. Revision exercise: classify sample review findings by risk and state what evidence should be recorded before escalating an uncertain case.
Model validation and release evidence
Posting: The engineer contributes to validation, regression testing, test reports, and release certification. Revision exercise: outline the evidence a test report should contain to explain a model-quality change and any unresolved defects.

Skills to revise

Inspect and compare generated images
Posting: Visual inspection and image-quality validation are required. Revision exercise: score a small set of images against one prompt and document specific artifacts, prompt mismatches, and consistency differences rather than giving only an overall impression.
Exercise a listed image platform
Posting: Hands-on experience with at least one listed platform, such as Adobe Firefly, Stable Diffusion, or OpenAI Image APIs, is required. Revision exercise: on a platform available to you, run a small prompt variation and record the inputs, outputs, and observed quality differences.
Test image-service REST APIs
Posting: REST API testing with Postman or a similar tool is required. Revision exercise: draft and run, where a suitable service is available, valid and invalid image-generation requests; inspect status codes, response structure, and error handling.
Automate AI-workflow regression checks
Posting: Python automation and experience with PyTest, Playwright, Selenium, or Robot Framework are required. Revision exercise: use Python and one listed framework to create repeatable checks for request handling and output properties, while leaving subjective visual judgments for documented review.
Document reproducible defects
Posting: The role documents defects, prepares test reports, and calls for debugging and documentation skills. Revision exercise: write a defect report for a prompt mismatch or enhancement artifact with inputs, reproduction steps, expected versus observed results, and an example output.

Your preparation checklist

Select a relevant testing walkthrough
Prepare a concise example from your experience testing an AI-powered application or generated images, if available. Show the goal, your role, the platform used, test approach, defects found, and outcome; distinguish your own work from the team’s.
Design an image-evaluation exercise
Draft a test matrix for prompts and features such as upscaling, restoration, Generative Fill, and background replacement. Include checks for prompt accuracy, consistency, sharpness, color, texture, artifacts, and realism, plus how you would compare model outputs.
Prepare an API automation example
Bring a small Python test example or explain an existing one that checks an image-generation or enhancement REST API. Cover request validation, error cases, repeatable regression runs, and how you would use a listed framework such as PyTest.
Outline a rights and Responsible AI review
Prepare an example review checklist for generated images covering logos, trademarks, branded assets, licensing, and other potential intellectual-property risks. Explain how you would document uncertain findings rather than claim a visual check proves compliance.
Show how you communicate release evidence
Prepare a redacted defect report and brief test summary, or sample versions if needed. Show reproduction details, affected prompts or outputs, severity rationale, regression results, and unresolved risks for model-validation discussions.
Ask focused questions about evaluation standards
Prepare questions about the image-quality criteria, model benchmarks, business requirements, Responsible AI review, and release-certification expectations the team uses. The posting names these responsibilities but does not specify its thresholds or process.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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