Jobs at Vertex, Inc.

Leads regional Technical Customer Support teams, overseeing performance, staffing, case management, escalations, service quality, and operational improvements. Uses support data, dashboards, and customer feedback to identify risks and drive measurable actions. Partners with Product, Development, Operations, Quality, and IT to resolve customer-impacting issues, improve supportability, and reduce recurring problems. Develops scalable workflows, quality controls, knowledge resources, and performance reporting while fostering an accountable, inclusive, customer-focused culture.

Leads multistate indirect tax research, tax content accuracy, legislative tracking, compliance testing, and AI governance for Vertex products. Manages content projects, automation initiatives, stakeholder collaboration, change management, and a tax research team. Evaluates AI-generated outputs for accuracy, bias, defensibility, and compliance while overseeing recruiting, development, and performance management.

Own the strategy, roadmap, and full product lifecycle for Vertex’s AI-powered indirect tax audit and notice management solution. Translate tax audit workflows into AI-enabled features, establish evaluation frameworks for accuracy and compliance, guide human-in-the-loop experiences, and collaborate with engineering, design, sales, marketing, consulting, and customers. The role requires deep expertise in audit evidence gathering and notice response workflows, strong product leadership, and the ability to drive cross-functional initiatives.

Own the end-to-end SAP product portfolio, including strategy, roadmap, discovery, requirements, backlog, delivery, AI initiatives, pricing, packaging, and commercialization. Provide applied SAP technical leadership across architecture, integrations, BTP, APIs, and cloud technologies. Develop value propositions, enable Sales and partner teams, and represent the portfolio in strategic customer, executive, and partner engagements while driving cross-functional execution.

Leads strategy, execution, and the full lifecycle for tax technology products, from ideation through launch and optimization. Conducts market research, aligns cross-functional teams, drives roadmap and innovation decisions, and identifies integration opportunities across product lines. Uses customer, market, and performance data to guide direction, mentors team members, represents Vertex at industry events, and maintains awareness of competitors. Requires extensive product management experience, SaaS and cloud expertise, strong business acumen, and occasional travel.

Generate revenue by selling Vertex transaction tax solutions to enterprise prospects and existing customers. Develop territory and account strategies, navigate complex organizations, build executive relationships, understand tax and technical environments, respond to RFPs, create tailored proposals, and close large software and consulting opportunities. Collaborate with sales, consulting, partners, and internal teams while maintaining strong knowledge of tax software, customer needs, and industry developments.

Own the full enterprise sales cycle across the Midwest, from prospecting and territory planning through negotiation and close. Build executive relationships, manage complex multi-stakeholder deals, align tax technology and enterprise software solutions to customer outcomes, and exceed revenue quotas. Use AI-enabled tools and data to improve pipeline development, forecasting, and sales productivity while collaborating with marketing, consulting, sales development, and customer success teams.

Delivers GTM commercial analysis, dashboards, reporting, forecasting, workflow automation, and AI-assisted solutions. Partners with commercial, Finance, Data & Analytics, IT, and regional teams to improve decision-making, standardize metrics, validate outputs, resolve data issues, and reduce manual work. Owns assigned initiatives, documents business logic, supports testing and adoption, and develops reusable capabilities across regions.

Lead enterprise model-training strategy for Commercial AI products: fine-tune LLMs (QLoRA/LoRA/PEFT), train traditional ML models, design large-scale data pipelines, enforce data governance and PII handling, build reproducible experiment/training pipelines, optimize GPU/distributed training costs, and mentor teams to operationalize models into production.

Lead design and implementation of the enterprise AI orchestration and retrieval platform: build MCP servers, define tool-surface and multi-agent patterns, design RAG systems and chunking, implement routing/context/window management and observability, set evaluation and safety standards, onboard product teams, and mentor engineers to raise orchestration and retrieval maturity.