Senior AI Tooling Engineer

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.

• 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.

• 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.