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Senior AI Tooling Engineer

Vizient · Chicago, IL · $117,600–$206,000 a year · Full time

Posted Aug 31, 2026 · We last checked this listing on Oct 5, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a tool you took through its whole lifecycle, from evaluation to retirement.

What they are checking: Ownership across intake, architecture, operations and lifecycle, which the posting stresses.

Tell me about a dashboard or report you built that leaders actually used to make a decision.

What they are checking: Turning tooling data into insight people act on, not just more charts.

What would you do

Data in an AI tooling dashboard doesn't match what a team sees in their own systems. How do you investigate?

What they are checking: Tracing source-to-platform data flows to find mapping or sync issues methodically.

Engineers worry that productivity metrics will be used against them. How do you handle that?

What they are checking: Sensitivity to trust and responsible use of engineering data, which affects adoption.

How do you decide whether to integrate a new AI tool or push back on adding another one?

What they are checking: Architecture assessment that weighs value, security, overlap and support burden.

About the work

How would you onboard a new engineering team's repositories and workflows into a platform like Jellyfish?

What they are checking: Practical integration skills across source systems, identity mapping and data accuracy in engineering analytics tools.

Walk me through how you'd instrument an application in Amplitude so the data answers real product questions.

What they are checking: Event taxonomy design and governance so analytics are trustworthy, not a pile of random events.

How would you measure whether AI coding tools are actually improving engineering productivity?

What they are checking: Thoughtful measurement that avoids vanity metrics and accounts for quality, not just speed.

What security and access concerns come up when connecting AI tooling platforms to source code and user data?

What they are checking: Awareness of least-privilege access, data exposure and vendor security review.

How do you build reusable patterns so supporting the next tool is faster than the last?

What they are checking: Engineering mindset beyond single-product administration, which the role explicitly expects.

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The full job description

As published by Vizient.

When you’re the best, we’re the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves . We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future. Summary In this role, you will provide hands-on engineering, integration, adoption, analytics, operations, and lifecycle management for Vizient’s enterprise AI tooling ecosystem. You will help enable secure, scalable, reliable, and measurable use of platforms supporting AI Engineering, AI Quality Engineering, LLMOps/AIOps, Product, Engineering, Data, Architecture, Security, Governance, and Delivery teams. The Senior AI Tooling Engineer will initially support platforms such as Jellyfish and Amplitude while applying reusable engineering practices across platform implementation, source-to-platform data flows, application onboarding, analytics, measurement, and lifecycle operations. This role extends beyond dashboard development or administration of a single product and will expand to additional AI tooling as organizational priorities evolve. The engineer will own technical work from requirements intake and architecture assessment through implementation, integration, instrumentation, validation, production readiness, analytics, adoption, operational support, optimization, and lifecycle change. Responsibilities • Own the implementation, configuration, administration, and production readiness of assigned enterprise AI tooling platforms. • Design, implement, and support integrations between enterprise source systems and AI tooling platforms. • Build and maintain APIs, SDK integrations, connectors, pipelines, event streams, automation, and synchronization workflows. • Partner with application and product teams to onboard internal and customer-facing applications. • Assess application architecture and define integration, instrumentation, identity, schema, event, and data requirements. • Develop and maintain event-tracking plans, taxonomies, naming conventions, properties, and instrumentation specifications. • Validate integrations, instrumentation, data completeness, accuracy, timeliness, lineage, sequencing, identity resolution, and schema consistency. • Troubleshoot complex platform, authentication, integration, performance, data-flow, and data-quality issues; perform root cause analysis and implement corrective actions. • Translate business, product, engineering, and AI-tooling objectives into measurable events, metrics, dashboards, analytics, and decision-support views. • Develop analytics related to engineering productivity, delivery flow, quality, investment allocation, application usage, feature adoption, engagement, retention, conversion, and user journeys. • Monitor platform usage, adoption, license utilization, data volume, cost, performance, operational health, and lifecycle risks. • Partner with Product, Engineering, Data, Architecture, Security, Governance, Privacy, Procurement, and business teams to support adoption, compliance, documentation, enablement, and continuous improvement. Qualifications • Bachelor’s degree in Computer Science, Engineering, Information Systems, Data/Analytics, or a related field, or equivalent practical experience. • Strong experience in software engineering, platform engineering, data engineering, analytics engineering, product analytics, or a closely related technical discipline. • Hands-on experience implementing, integrating, configuring, or operating enterprise SaaS, engineering, analytics, developer-productivity, or AI tooling platforms. • Experience with APIs, SDKs, connectors, event streams, data pipelines, integrations, and application instrumentation. • Strong proficiency with SQL and the ability to validate platform and analytics data against source systems. • Experience troubleshooting complex platform and data-flow issues using structured root cause analysis and corrective action. • Experience developing metrics, dashboards, and analytical views that support product, engineering, operational, or business decisions. • Ability to assess application architecture and translate stakeholder needs into technical integration, instrumentation, data, and analytics requirements. • Experience working cross-functionally with Product, Engineering, Data, Architecture, Security, Governance, and business teams in a large-enterprise environment. • Strong documentation, communication, stakeholder-management, prioritization, and problem-solving skills. • Ability to independently manage work from requirements intake through implementation, validation, documentation, handoff, and ongoing support. • Ability to identify adoption barriers, workflow friction, data-quality issues, lifecycle risks, cost inefficiencies, and optimization opportunities. Preferred Qualifications • Hands-on experience with Jellyfish, Amplitude, or comparable engineering intelligence and product analytics platforms. • Advanced experience with product analytics, experimentation, session replay, customer-data platforms, feature-management tools, or developer-productivity platforms. • Experience with JavaScript, TypeScript, Python, Java, or another enterprise programming language. • Experience with Azure and enterprise identity, authentication, and access-management patterns. • Experience with Databricks, Power BI, or comparable data and analytics technologies. • Experience supporting AI applications, conversational products, internal developer tools, copilots, or enterprise AI platforms. • Familiarity with LLM applications, prompt workflows, AI observability, model telemetry, latency, reliability, token consumption, and AI cost measurement. • Experience in healthcare, regulated industries, or complex large-enterprise environments. #LI-JB1 Estimated Hiring Range: At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $117,600.00 to $206,000.00. This position is also incentive eligible. Vizient has a comprehensive benefits plan! Please view our benefits here: http://www.vizientinc.com/about-us/careers Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.

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