New opportunity

AI Technical Product Owner

Allied Benefit Systems · Chicago, United States

About this role

The AI Technical Product Owner leads Allied Benefit Systems’ internal AI execution, working with engineering, data science and business teams to deploy generative AI, predictive modeling and intelligent automation in business workflows. The role is listed in Chicago, IL, with a remote work environment.

Responsibilities

Deliver AI capabilities for decision support, semantic search, predictive modeling and generative AI, with measurable business value.
Own product requirements, AI roadmaps, priorities and sprint plans. Coordinate full-stack engineers, data scientists and ML engineers to deliver solutions on time, and turn QA feedback and test results into product improvements.
Gather functional and non-functional requirements, including latency and uptime expectations, from business subject-matter experts. Plan deployments around business cycles and identify opportunities to modernize legacy processes with AI.
Brief senior leadership on progress and business impact using data and analysis. Lead stakeholder engagement, train subject-matter experts and manage change as AI-driven processes are introduced.
Ensure solutions follow Allied’s responsible AI governance policies and are ISO 42001 audit-ready.

Requirements

Bachelor’s degree in a related field or equivalent work experience; at least 7 years in product leadership roles, such as Product Owner or Product Manager, with program management experience in technology projects.
Understanding of machine-learning fundamentals and exposure to generative AI architectures, including retrieval-augmented generation, vector databases and orchestration frameworks. Cross-functional experience in product management, engineering processes and business analytics or FP&A is required.
Executive-level communication and the ability to examine data flows, APIs and model outputs to troubleshoot or guide decisions without hands-on coding. The posting identifies Jira, Azure DevOps, Azure Machine Learning, related cloud services, Confluence, Power BI and Excel as relevant tools. Prior AI or machine-learning product management experience is a strong plus.

Compensation and working arrangements: The listed base salary is $135,000–$150,000 annually. Benefits include medical, dental, vision, life and disability insurance, paid time off, tuition reimbursement, an employee assistance program and a technology stipend. Remote work requires cable broadband or fiber-optic internet with speeds of at least 100 Mbps download and 25 Mbps upload.

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

Skills for this role

AI product managementProduct roadmappingProgram managementAgile project managementMachine learningGenerative AIPredictive modelingIntelligent automationSemantic searchRetrieval-augmented generationVector databasesAI orchestration frameworksAzure Machine LearningMLOpsAPIsJiraAzure DevOpsConfluencePower BIExcelBusiness analyticsStakeholder managementExecutive communicationRequirements gatheringChange managementResponsible AI governanceISO 42001

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

Deliver measurable AI value
The role owns AI capabilities for decision support, semantic search, predictive modeling and generative AI in real workflows. Connect use-case priorities to measurable business outcomes, not just model performance.
Own the roadmap through release
Define requirements, priorities and sprint plans; coordinate full-stack, data science and ML engineering teams for on-time delivery; and turn QA results into refined requirements and product improvements.
Fit AI to business operations
Work with business subject-matter experts on functional needs, latency and uptime expectations. Identify improvements to legacy processes, plan deployments around busy and slow seasons, and support training and change management.
Make progress legible to leadership
Brief senior leaders regularly with data-backed plans that translate technical progress into business impact. The posting also calls for business analytics or FP&A experience and tools for tracking metrics and ROI.
Build in responsible AI governance
Ensure solutions follow Allied’s responsible AI policies and are ISO 42001 audit-ready. This is an explicit delivery responsibility, not an optional review after launch.

Likely challenges to think through

Balancing release speed with operational performance
A likely difficulty is keeping an AI delivery schedule while meeting SME expectations for latency and uptime and responding to QA findings. Prepare a trade-off example that shows how you would set acceptance criteria, assess test results and decide what must change before release.
Introducing AI into established workflows
Legacy-process integration may offer substantial value but compete with deployment timing and SME readiness, especially across busy and slow seasons. Prepare to explain how you would scope a workflow change, involve SMEs and plan training without assuming immediate adoption.
Preserving governance while iterating
Iterative GenAI and predictive-modeling work may create tension between learning quickly and keeping solutions compliant and audit-ready. Prepare to describe how governance expectations would shape requirements, release decisions and evidence of testing; the posting does not specify Allied’s particular controls.

Practice your answers

Technical practice

How would you decide which internal AI use case to pursue first?

What to coverSuggested answer focus: compare decision support, search, prediction and automation opportunities by business value, feasibility and risk. Explain how you would establish a baseline and measure whether the delivered capability improved the workflow.

How would you turn an AI roadmap into sprint priorities?

What to coverSuggested answer focus: define outcomes and scope, identify dependencies across engineering and data science, and break uncertain work into testable increments. Describe how you would adjust priorities when evidence changes while keeping delivery commitments clear.

How would you evaluate a semantic-search or retrieval-augmented generation approach?

What to coverSuggested answer focus: explain how retrieval, a vector database and generated responses fit together, then compare answer usefulness, source relevance, latency and operating cost. Discuss when search alone might meet the need without generated answers.

How would you assess a predictive model for decision support?

What to coverSuggested answer focus: define the decision and baseline first, then select evaluation measures that reflect the consequences of different errors. Address data quality, performance on new data and how people would use the prediction in the workflow.

What would you ask engineers about a generative-AI service integration?

What to coverSuggested answer focus: trace the data flow through APIs, retrieval or other services, model outputs and any orchestration components. Discuss reliability, response time and where failures or unexpected outputs would become visible to users.

How would you specify requirements and time a deployment around business cycles?

What to coverSuggested answer focus: work with subject-matter experts to define the workflow, acceptance criteria, latency and uptime expectations. Explain the trade-off between releasing during a busy period and delaying until there is room to test, train users and respond to issues.

How would you investigate poor model outputs reported by QA without coding?

What to coverSuggested answer focus: compare the reported output with the input, upstream data, API responses and expected behavior to narrow the cause with technical partners. Show how you would turn test evidence into a reproducible issue, a product decision and refined requirements.

What would you expect to review before and after deploying an ML capability?

What to coverSuggested answer focus: discuss model evaluation, deployment readiness, ownership and monitoring in terms a product owner can act on. The posting names Azure Machine Learning and related cloud services; explain how you would use information from those systems to guide decisions rather than imply hands-on coding is required.

How would you make an AI product governance-ready?

What to coverSuggested answer focus: explain how you would check a solution against Allied’s responsible AI policies and retain traceable requirements, reviews, test results and decisions for ISO 42001 audit readiness. Avoid assuming the posting specifies particular controls or a certification process.

How would you evaluate adding AI to a legacy workflow?

What to coverSuggested answer focus: map the current process, its failure points and the outcome worth improving before choosing intelligent automation or another AI capability. Compare integration effort, operational risk and measurable gains with a less complex process improvement.

Behavioural practice

Tell me about a time you aligned product, engineering and data-science partners on delivery.

What to coverSuggested answer focus: give the situation, conflicting priorities or dependencies, your actions to clarify scope and ownership, and the result. Be specific about how you kept delivery moving without obscuring uncertainty.

Describe a time you had to explain technical progress or a setback to senior leadership.

What to coverSuggested answer focus: state the situation, how you translated technical evidence into business impact and a decision, and the result. Include the data you used and how you handled questions or changed expectations.

Tell me about a time you helped subject-matter experts adopt a changed workflow.

What to coverSuggested answer focus: describe the situation, what you learned from the people affected, your training or change-management actions, and the result. Distinguish initial rollout from evidence that the new process was actually being used.

Describe a time testing or user feedback caused you to change a product plan.

What to coverSuggested answer focus: explain the original plan, the evidence that challenged it, how you worked with the team to revise requirements or priorities, and the result. Make clear what you communicated to stakeholders about the trade-off.

Tell me about a time you raised a quality or governance concern despite delivery pressure.

What to coverSuggested answer focus: set out the situation, the risk you identified, the actions you took to seek review or mitigation, and the result. Explain how you balanced accountability for delivery with the need for a defensible decision.

Sharpen your knowledge

Topics to prepare

ML fundamentals and predictive modeling
The posting calls for ML fundamentals and delivery of predictive modeling and decision-support capabilities. Suggested revision exercise: explain how you would compare a model’s test results with the business decision it is meant to improve, including when you would defer release.
Generative AI and semantic search architecture
The posting names retrieval-augmented generation, vector databases and orchestration frameworks. Suggested revision exercise: diagram a semantic-search request from source data through retrieval and model output, then identify where poor results could arise.
AI product value and prioritization
The role owns AI roadmaps and priorities and must demonstrate measurable business value. Suggested revision exercise: compare two hypothetical opportunities to modernize a legacy workflow, stating the expected benefit, delivery dependencies and reason for your priority order.
Deployment, reliability and business timing
The role captures latency and uptime expectations, plans around busy and slow seasons, and works with Azure Machine Learning and related services for deployment and MLOps. Suggested revision exercise: explain the trade-offs in releasing a model update during a busy business period versus a slower one.
Responsible AI governance and audit readiness
The posting requires compliance with Allied’s responsible AI policies and ISO 42001 audit readiness; it does not specify the policies’ contents. Suggested revision exercise: outline the questions and evidence you would seek from the relevant teams before approving an AI release, without assuming Allied’s controls.

Skills to revise

Write testable product requirements
The role gathers functional and non-functional requirements from business SMEs. Suggested practice exercise: draft a one-page requirement for a hypothetical decision-support workflow, with a user need, acceptance criteria, latency and uptime questions, and unresolved assumptions.
Manage a delivery backlog
The posting names sprint plans, release management, Jira and Azure DevOps. Suggested practice exercise: turn the hypothetical workflow into a prioritized backlog, showing dependencies across full-stack, data science and ML engineering work and explaining a scope trade-off.
Convert QA findings into product changes
The role turns QA feedback and test results into refined requirements. Suggested practice exercise: take a hypothetical failed search-result test, write a clear issue, revise its acceptance criteria and decide whether it blocks release.
Triage an AI issue without coding
The posting expects examination of data flows, APIs and model outputs without hands-on coding. Suggested practice exercise: trace a hypothetical incorrect model response through those three areas and prepare targeted questions for engineers.
Brief leadership with business metrics
The role briefs senior leadership using data and analysis; the posting names Power BI and Excel for metrics and ROI. Suggested practice exercise: prepare a short update using hypothetical adoption, cost and outcome figures, distinguishing observed results from forecasts.
Plan SME training and change
The posting calls for stakeholder engagement, SME training and change management as AI-driven processes are introduced. Suggested practice exercise: outline a rollout discussion for a hypothetical legacy-workflow change, including SME feedback, training needs and how concerns would reach the product backlog.

Your preparation checklist

Map evidence to the role
The posting requires at least seven years of product leadership, technology program management, and cross-functional work. Prepare a one-page evidence map with specific examples you can substantiate; distinguish required experience from prior AI product management, which is a strong plus.
Build a project walkthrough
Prepare one relevant, truthful example showing the business problem, your product decisions, coordination with engineering and data science, delivery trade-offs, and measured outcome. Allied emphasizes AI capabilities with measurable business value.
Practice an AI workflow design
For a hypothetical semantic-search or decision-support use case, sketch the user workflow, data flow, APIs, model outputs, and where retrieval-augmented generation or predictive modeling would fit. Label assumptions rather than presenting the design as an Allied project.
Draft requirements and a delivery plan
Turn the hypothetical use case into sample functional requirements, latency and uptime expectations, a prioritized backlog, and a short roadmap. Show how QA findings would change priorities and how a release might be timed around busy and slow business cycles.
Prepare a technical troubleshooting example
Rehearse how you would examine data flows, APIs, and model outputs to narrow down a quality or reliability issue without relying on hands-on coding. Explain what you would ask engineers or ML engineers to verify and how the findings would affect product decisions.
Outline governance and adoption safeguards
Prepare a concise plan for documenting responsible-AI decisions and audit evidence, then training subject-matter experts and collecting feedback. The posting calls for compliance with Allied’s policies and ISO 42001 audit readiness; do not assume what its internal controls are.
Rehearse an executive update
Create a brief example update that connects progress, risks, and a proposed decision to business metrics or ROI. The role requires regular senior-leadership briefings backed by data and analysis.
Write targeted employer questions
Ask which internal AI use cases are prioritized, how success is measured, who sets latency and uptime expectations, how responsible-AI reviews work, and how deployment timing is coordinated with business cycles.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in United States. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…