New opportunity

AI Artist (2D Generalist ) - Bingo Bash

Scopely · Bangalore, India

About this role

Scopely is seeking an AI Artist (2D Generalist) for its Bingo Bash team in Bangalore, India, on a hybrid basis. The role creates visual assets for a free-to-play mobile game, using AI to support creative production while maintaining high-quality, hand-crafted aesthetics and a consistent artistic style. This is a full-time role with a 40-hour workweek.

Responsibilities

  • Train and fine-tune AI art models to produce stylized outputs aligned with the project's artistic vision. Generate and enhance game assets using Stable Diffusion, Flux, and proprietary or licensed tools, and integrate AI-generated content into production pipelines.
  • Work with artists, tech artists, leadership, and production teams to explore and refine visual styles and ensure assets meet project objectives. Document workflows, share feedback with internal and external artists, improve processes, and investigate new AI-art approaches.

Requirements

  • 2+ years of experience in AI art and 2D art within the game industry, plus a portfolio demonstrating composition, color, and style consistency.
  • Practical experience with generative art tools such as Stable Diffusion, ComfyUI, or A1111; proficiency in Photoshop and Illustrator; understanding of game asset pipelines and mobile optimization; and familiarity with dataset curation, tagging, and model-training workflows.
  • Clear communication, collaboration, attention to detail, brand and style fidelity, and willingness to experiment with new creative workflows.

Preferred

Experience with Blender or ZBrush, basic 3D modeling or animation knowledge, mobile game development experience, and familiarity with Jira and ClickUp workflows.

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

Skills for this role

AI art2D artAI model trainingModel fine-tuningStable DiffusionFluxComfyUIA1111PhotoshopIllustratorGame asset pipelinesMobile asset optimizationDataset curationData taggingCompositionColorStyle consistencyCommunicationDocumentationBlenderZBrush3D modelingAnimationMobile game developmentJiraClickUp

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Preserve a hand-crafted visual style

    The role calls for high-quality game art with strong composition, color, and style consistency. Prepare portfolio examples that show how AI-assisted assets were refined to fit an established look.

  • Train models for stylized output

    Training and fine-tuning AI art models is a stated duty, supported by dataset curation and tagging. Be ready to explain how dataset choices and iteration affect style fidelity.

  • Deliver assets through a mobile game pipeline

    The posting calls for creating assets with tools including Stable Diffusion and Flux, integrating AI-generated content into production, and understanding mobile optimization. Prepare an example that follows an asset from generation through final production-ready art.

  • Collaborate and document decisions

    The artist works with leadership, art, tech art, and production teams, shares feedback with internal and external artists, and documents workflows. Prepare to describe how visual feedback becomes clear, actionable revisions.

  • Explore useful new workflows

    Researching AI-art advances and improving creative processes are explicit duties. Focus preparation on how to evaluate a new approach against artistic quality and production usefulness, rather than novelty alone.

Likely challenges to think through

  • Novelty versus style fidelity

    Likely preparation hypothesis: AI tools may produce appealing variations that do not fit Bingo Bash’s established art direction. Prepare an example of selecting and refining outputs against composition, color, and style goals.

  • Visual detail versus mobile constraints

    Likely preparation hypothesis: an asset can look polished in isolation yet need changes for mobile use. Prepare to explain how you would retain readability and artistic intent while adapting an asset for a mobile pipeline.

  • Experimentation versus repeatable production

    Likely preparation hypothesis: trying new models or workflows may compete with the need to deliver consistent assets and communicate changes across teams. Prepare an example of documenting settings, decisions, and feedback so an approach can be assessed and reused.

03

Practice your answers

Technical practice

How would you assess composition, color, and style consistency in a proposed Bingo Bash asset?

What to coverSuggested answer focus: Explain how you would compare the asset with an established visual direction and check readability, visual hierarchy, and consistency. Distinguish artistic judgment from any checks you would seek from the art team.

How would you curate and tag a dataset for training a stylized AI art model?

What to coverSuggested answer focus: Discuss selecting representative, high-quality examples and using consistent tags that describe the intended style. Explain how you would identify gaps or conflicting examples before training.

How would you fine-tune an AI art model toward a project's artistic vision?

What to coverSuggested answer focus: Describe an iterative approach to training, reviewing outputs against style references, and adjusting the dataset or training approach. Address the trade-off between stylistic consistency and enough variety for fresh game content.

How would you choose a workflow for generating assets with Stable Diffusion, Flux, or a generative-art interface?

What to coverSuggested answer focus: Compare workflows by the control they offer, output quality, repeatability, and ease of refinement. The posting names these tools but does not prescribe a particular interface or production setup.

What would you do if generated assets repeatedly drifted from the intended style?

What to coverSuggested answer focus: Separate possible causes in the source examples, tags, model, and generation settings, then describe how you would test changes one at a time. Include when you would seek artistic feedback rather than keep adjusting the model.

How would you turn an AI-generated image into an asset with a hand-crafted finish?

What to coverSuggested answer focus: Describe the artistic corrections you might make in Photoshop or Illustrator, such as refining shapes, color, and details. Explain how you would decide whether an output is worth refining or should be regenerated.

How would you integrate AI-generated artwork into a game-asset production pipeline?

What to coverSuggested answer focus: Walk through review, refinement, preparation, and handoff to artists or tech artists. Identify what information about the asset and its creation would help others iterate on it.

What would you check when preparing a 2D asset for a mobile game?

What to coverSuggested answer focus: Discuss visual clarity at the intended display size and the need to balance quality with mobile asset constraints. Avoid assuming specific file formats, budgets, or engine requirements that the posting does not state.

How would you evaluate whether a batch of AI-generated assets meets project objectives?

What to coverSuggested answer focus: Propose a review that considers style fidelity, artistic quality, consistency across the batch, and usefulness to the production team. Explain how you would capture feedback and decide which assets to refine, reject, or use.

How would you assess a new AI-art workflow before adopting it for creative production?

What to coverSuggested answer focus: Compare it with the current approach on asset quality, controllability, and effort to incorporate into the pipeline. Treat this as a suggested evaluation method; the posting asks for exploration but specifies no approval process.

Behavioural practice

Tell me about a time you aligned artwork with feedback from different teams.

What to coverSuggested answer focus: Use a real situation involving collaborators, the actions you took to clarify or reconcile their input, and the result. The role calls for collaboration with artists, tech artists, leadership, and production; do not imply every group was involved in your example.

Tell me about a time you gave useful feedback to another artist.

What to coverSuggested answer focus: Describe the situation, how you made the feedback specific and respectful, and what changed as a result. An example involving either an internal or external collaborator is relevant to the posting.

Tell me about a time you documented or improved a creative workflow.

What to coverSuggested answer focus: Explain the initial difficulty, what you documented or changed, and the resulting effect on others' work. Keep the example grounded in your actual experience rather than assuming Scopely uses a particular process.

Tell me about a time you experimented with an unfamiliar creative tool or workflow.

What to coverSuggested answer focus: Set out why you tried it, how you evaluated its results, and what you decided to do next. Connect the example to the role's stated interest in exploring new AI-art approaches.

Tell me about a time you caught a visual inconsistency before an asset was finished.

What to coverSuggested answer focus: Describe the situation, the detail you noticed, the action you took, and the result. Focus on artistic judgment and style fidelity rather than presenting a hypothetical success as personal experience.

02

Sharpen your knowledge

Topics to prepare

  • Composition, color and style consistency

    The posting asks for a portfolio showing these art fundamentals and for assets that maintain a hand-crafted aesthetic. Revision exercise: compare three 2D game assets and explain how their focal points, palettes and rendering choices create a coherent style.

  • Dataset curation and model refinement

    The role involves training and fine-tuning stylized AI art models; familiarity with dataset curation and tagging is listed. Revision exercise: outline how you would select, tag and review a small set of permitted reference images, then judge whether model outputs match the intended style.

  • Game asset pipelines and mobile optimization

    The posting calls for understanding production pipelines and optimization for mobile. Revision exercise: trace a 2D asset from generated draft through artist review, finishing and mobile-ready delivery; identify where visual quality or consistency could be lost.

  • AI-assisted art direction

    The role uses AI to explore visual styles while preserving the project's artistic vision. Revision exercise: define criteria for accepting, revising or rejecting generated variants based on composition, style fidelity and visible artifacts.

  • Cross-team iteration

    The artist works with art, tech art, leadership and production teams and shares feedback with internal and external artists. Revision exercise: prepare a brief critique of a sample asset that states the visual issue, proposed change and question needing another team's input.

Skills to revise

  • Generate and compare asset variants

    Stable Diffusion and Flux are named for asset generation; ComfyUI and A1111 are listed as examples of practical tool experience. Suggested exercise: use an available named tool to produce several variants of one 2D game asset, then record which changes improve style consistency and which need manual correction.

  • Practice a model-refinement cycle

    Training and fine-tuning stylized models is an explicit responsibility. Suggested exercise: with permitted images and a tool you can access, curate and tag a small set, run a refinement test, and compare outputs against a fixed visual brief. No particular training method is specified in the posting.

  • Finish assets in Photoshop and Illustrator

    The posting names proficiency in both tools and emphasizes high-quality, hand-crafted results. Suggested exercise: clean up a generated 2D asset, correct distracting details, and prepare a matching companion element; explain the edits that made the pair visually consistent.

  • Check assets at mobile display sizes

    Mobile optimization and game asset pipelines are explicit requirements. Suggested exercise: inspect a finished asset at small display sizes, revise details that become illegible, and compare the revised version with the original.

  • Document an iterative workflow

    Clear documentation, feedback and process improvement are stated responsibilities. Suggested exercise: write a short record of an asset's inputs, generated variants, artist edits, feedback and final decision so another artist could understand the choices.

04

Your preparation checklist

  • Select a focused portfolio set

    Choose existing 2D game-art examples that show composition, color, and consistent style. Prepare brief notes on your contribution and how each asset fits its visual direction; these are stated portfolio priorities.

  • Build a model-training walkthrough

    Prepare one real example, if available, showing dataset curation, tagging, fine-tuning decisions, iterations, and how you judged style fidelity. The role explicitly includes model training and refinement.

  • Show the path from AI output to finished art

    Walk through an existing Stable Diffusion, Flux, ComfyUI, A1111, or equivalent workflow, then show any Photoshop or Illustrator finishing work. Label generated and hand-crafted contributions clearly.

  • Explain a mobile asset handoff

    Prepare a concise diagram or example tracing a 2D asset from creation through review, integration, and mobile optimization. The posting asks for game-pipeline and mobile-optimization understanding.

  • Practice a Bingo Bash-style design exercise

    As preparation, sketch a hypothetical brief for fresh bingo, cards, or slots content. Produce a small set of variations and explain how you would refine them for a cohesive style without presenting them as actual team assets.

  • Rehearse a collaboration and documentation example

    Outline a real instance, if available, of incorporating artist, tech-art, or production feedback. Bring a short sample workflow note or feedback summary; the role calls for cross-team iteration and clear documentation.

  • Prepare questions for the team

    Ask how the team defines style fidelity, reviews AI-assisted assets, integrates them into production, and decides which new AI-art workflows to explore. These questions follow the advertised responsibilities rather than assuming a specific process.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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