New opportunity

AI Engineer - AI & Automation

eBay · Austin, United States of America

About this role

The AI Engineer is a hands-on developer in eBay’s Finance Innovation & Transformation (FIT) team, building production-ready AI, machine learning and intelligent automation solutions for Finance. The role turns business problems into scalable capabilities that improve efficiency, strengthen controls and support decision-making.

Responsibilities

Partner with Finance and cross-functional stakeholders to define solution approaches, develop technical and system designs, and deliver solutions from concept through deployment.
Build production-grade models, intelligent agents, web applications, automation workflows and decision-support tools using Python, SQL and modern AI/ML frameworks.
Develop and maintain data pipelines, model workflows and reusable components; monitor and refine deployed solutions for performance, reliability, usability and adoption.
Evaluate emerging AI technologies and AI-assisted development tools, applying them where they create measurable business value. Collaborate with Finance, Product, Engineering, ITSS and other teams throughout delivery.

Required skills and experience:

Proven experience delivering end-to-end AI, machine learning and intelligent automation solutions, including design, data preparation, deployment and optimization. Strong Python, SQL, problem-solving, communication and stakeholder-management skills are required.
Experience handling big data, including Hadoop or Teradata, and working in agile environments. Ability to build maintainable solutions, work through ambiguity and collaborate across global teams.

Desired qualifications:

4+ years designing and delivering AI, machine learning and intelligent automation solutions in complex enterprise environments. Experience delivering production-ready solutions in regulated or controls-focused environments is preferred; Google BigQuery experience is a plus.

The position is listed in Austin, Texas, as a full-time role of 40 hours per week. The expected base pay range is $85,600–$151,100; an offer may also include a target bonus, restricted stock units and benefits, as applicable.

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

Skills for this role

PythonSQLMachine LearningArtificial IntelligenceIntelligent AutomationSystem DesignData PipelinesWeb Application DevelopmentAI AgentsBig DataHadoopTeradataAgileGoogle BigQueryStakeholder ManagementCommunicationProblem Solving

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

Translate Finance needs into technical designs
The FIT role partners with Finance, Product, Engineering and ITSS to turn prioritized business problems into scalable system designs. Prepare to explain how you would define the problem, align stakeholders and choose a solution approach.
Deliver solutions end to end
The posting calls for hands-on design, data preparation, development, deployment and optimization of AI, machine learning and intelligent automation solutions. Prepare to walk through those stages, including decisions made with Python, SQL and AI/ML frameworks.
Build tools that fit business processes
The role develops intelligent agents, web apps, automation workflows and decision-support tools that move beyond analysis into execution. Focus preparation on how a tool would be usable within a Finance workflow, not just how its model works.
Engineer reusable data and model foundations
The role maintains data pipelines, model workflows and reusable components for scalability and reliability; it also calls for big-data experience, including Hadoop or Teradata. Prepare to discuss maintainable designs and data-processing choices.
Improve deployed solutions and assess new technology
The posting calls for monitoring performance, reliability, usability and adoption, and evaluating emerging AI and AI-assisted development tools for measurable business value. Prepare to explain how you would judge whether to refine, scale or adopt a solution.

Likely challenges to think through

Balancing automation with Finance controls
Preparation hypothesis, not a confirmed eBay problem: faster automated execution may need to be weighed against the role’s aim to strengthen controls. Prepare an example of how you would define safeguards and measure efficiency without assuming a particular control process.
Handling varied data at production scale
Preparation hypothesis: combining structured and unstructured data with big-data systems may complicate data preparation and reliable deployment. Prepare to explain trade-offs among data quality, processing cost, maintainability and model performance.
Securing adoption across teams
Preparation hypothesis: a technically sound tool may still be hard to integrate into a Finance process involving multiple functions and global teams. Prepare to describe how you would resolve differing needs, make the workflow usable and use feedback after deployment.
Choosing emerging AI tools judiciously
Preparation hypothesis: a newer tool could accelerate delivery but add reliability or maintenance risk. Prepare to compare it with a simpler approach using the posting’s criteria of measurable value, scalability and ongoing performance.

Practice your answers

Technical practice

How would you turn a Finance problem into a scalable AI or automation design?

What to coverThe role spans stakeholder discovery, system design, and deployment. Preparation suggestion: explain how you would define the decision or process to improve, compare solution approaches, and agree on measures of business value and controls.

How would you prepare structured and unstructured data for a Finance AI use case?

What to coverThe posting mentions both data types and end-to-end data preparation. Preparation suggestion: discuss data quality, suitability for the proposed model or automation, and how you would detect problems before deployment.

How would you use Python and SQL to build a maintainable solution?

What to coverStrong Python and SQL skills are required for production-grade AI and automation work. Preparation suggestion: describe how you would divide data processing and application logic, test the result, and make components reusable.

How would you design a reliable pipeline for large Finance datasets?

What to coverThe role includes robust data pipelines and experience handling big data, with Hadoop or Teradata named in the posting. Preparation suggestion: address data validation, scale, failure recovery, and trade-offs between processing approaches without assuming a particular platform is used by the team.

How would you take a machine learning model from development to production?

What to coverThe posting calls for model workflows, deployment, and ongoing optimization. Preparation suggestion: walk through evaluation before release, integration with the intended business process, and how you would make later refinements safely.

When would an intelligent agent or automation workflow be appropriate for a Finance process?

What to coverThe role builds agents and workflows that move beyond analysis into execution and strengthen controls. Preparation suggestion: compare automation with a simpler tool, then explain how you would bound actions, handle exceptions, and keep outcomes reviewable.

How would you design a web app or decision-support tool for sustained use?

What to coverThe posting names web apps, decision-support tools, usability, and long-term adoption. Preparation suggestion: discuss how the tool would fit an existing process, present useful outputs, and gather evidence that people can use it effectively.

How would you troubleshoot a deployed AI solution whose results or reliability have declined?

What to coverMonitoring and refining deployed solutions for performance, reliability, and evolving business needs are stated duties. Preparation suggestion: separate data, model, application, and workflow causes, then explain how you would assess impact and verify a fix.

How would you evaluate an emerging AI technology or AI-assisted development tool?

What to coverThe role calls for applying such tools where they accelerate implementation and create measurable business value. Preparation suggestion: compare a new approach with the current solution on quality, maintainability, controls, and measurable outcomes.

Behavioural practice

Tell me about a time you clarified an ambiguous business problem before building a solution.

What to coverThe posting emphasizes working through ambiguity and translating Finance needs into technical designs. Preparation suggestion: use a real situation, your actions to define the problem with stakeholders, and the resulting decision or outcome.

Describe a time you aligned business and technical stakeholders on a solution approach.

What to coverThe role partners across Finance, Product, Engineering, ITSS, and other functions. Preparation suggestion: give the situation, how you explained trade-offs and resolved differing priorities, and the result.

Tell me about a time you delivered a solution while collaborating across teams.

What to coverThe posting calls for a hands-on contributor who works effectively with global, cross-functional teams. Preparation suggestion: identify your own actions, how you coordinated dependencies, and what the team delivered.

Describe a time feedback after deployment changed what you built.

What to coverThe role includes refining solutions for usability, adoption, and changing business needs. Preparation suggestion: explain the situation, how you gathered and acted on feedback, and the observable result.

Tell me about a time you balanced delivery speed with reliability or controls.

What to coverThe FIT team aims to improve efficiency while strengthening controls and delivering production-ready capabilities. Preparation suggestion: use a real example to explain the competing pressures, the action you took, and the outcome.

Sharpen your knowledge

Topics to prepare

End-to-end solution design
Posting: The role takes Finance problems from technical and system design through data preparation, deployment, and optimization. Suggested exercise: Sketch an architecture for a hypothetical Finance decision-support tool and explain the design choices at each stage.
AI and automation use-case selection
Posting: The team builds models, intelligent agents, web apps, and automation workflows for Finance, working with structured and unstructured data. Suggested exercise: Compare two hypothetical Finance tasks and explain when a model, agent, or simpler automation would be appropriate.
Big-data design
Posting: Experience handling big data, including Hadoop or Teradata, is required; Google BigQuery is a plus. Suggested exercise: Explain how data volume would affect a pipeline’s design and how you would check query performance and data quality.
Production reliability and controls
Posting: FIT aims to strengthen controls, and the engineer monitors solutions for performance, reliability, usability, and adoption. Suggested exercise: Define success measures, failure modes, and monitoring checks for a hypothetical deployed Finance workflow.
Stakeholder alignment in agile delivery
Posting: The engineer works with Finance, Product, Engineering, ITSS, and global teams; agile experience and clear communication are required. Suggested exercise: Turn an ambiguous business request into a proposed scope, open questions, and a short explanation of technical tradeoffs for nontechnical stakeholders.

Skills to revise

Python and SQL implementation
Posting: Strong Python and SQL skills are required. Suggested exercise: Prepare a small hypothetical Finance dataset with SQL, then use Python to validate it and produce a reproducible input for a model or automation workflow.
Data pipeline construction
Posting: The engineer builds robust data pipelines, model workflows, and reusable components. Suggested exercise: Implement a small pipeline with input validation, reusable transformations, and a check that detects changed or missing data.
Model development and evaluation
Posting: The role builds and deploys production-grade AI/ML solutions using modern frameworks. Suggested exercise: Build a small model for a hypothetical decision-support task, document its evaluation criteria, and explain how you would refine it after deployment.
Workflow integration
Posting: Agents, web apps, and automation workflows should integrate into business processes rather than stop at analysis. Suggested exercise: Prototype one small workflow that accepts an input, produces a reviewable recommendation, and shows where a person would act on it.
Monitoring and iteration
Posting: Deployed solutions are monitored and refined for performance, reliability, usability, and adoption. Suggested exercise: Add checks to a prototype for failures and output quality, then describe what evidence would prompt a change.

Your preparation checklist

Prepare an end-to-end project walkthrough
Select a real AI, ML, or automation project and outline the business problem, system design, data preparation, Python and SQL work, deployment, and subsequent optimization. State your own contributions and measured results; the posting emphasizes delivery from concept through production.
Practice a Finance solution design
Draft a hypothetical design for a Finance process that needs greater efficiency or stronger controls. Show how you would clarify the problem with stakeholders, choose between a model, agent, web app, or workflow, and plan integration, scalability, and maintainability. Present it as an exercise, not an eBay project.
Assemble technical evidence
Prepare specific examples of Python, SQL, modern AI/ML frameworks, and large-scale data handling you have used. Be ready to explain trade-offs and distinguish experience with Hadoop, Teradata, or BigQuery rather than treating them as interchangeable.
Explain how a solution reaches production
Sketch a data pipeline and model workflow from inputs to a reusable component and business-facing output. Identify how you would test reliability and handle structured or unstructured data where relevant; these are concerns named in the posting.
Document monitoring and business outcomes
For a deployed example, prepare a short account of what you monitored, what you changed, and how you assessed performance, usability, adoption, or business value. If discussing a proposed approach, label the measures as proposed rather than achieved.
Rehearse a cross-functional delivery example
Outline a real situation involving ambiguous requirements or changing priorities. Explain how you communicated technical options, aligned stakeholders, and delivered as a hands-on contributor in an agile or cross-functional setting.
Write questions for the FIT team
Prepare questions about its prioritized Finance use cases, available data, control expectations, deployment ownership, and how success and adoption are measured. The posting names Finance, Product, Engineering, and ITSS as collaborators; ask how their responsibilities intersect in practice.

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

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