Vice President of Research & Development

We need to output HTML fragment with sections: headings

for sections present. Need to include sections only if posting has real specific content. The posting includes many bullet points under headings like "AI Fluency and Agentic Delivery", "Technical Roadmap and AI-Driven Opportunity", "Delivery and Execution", "Product Quality and Security", "Team Leadership and Development", "Financial and Resource Ownership", "Platform Modernization", "Stakeholder and Executive Communication", "Experience And Qualifications", "Performance Measurement Success In This Role Is Measured On The Following", and then salary range. We need to map these to allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. The posting does not have explicit headings like those; we need to decide which sections to include based on content. The instruction: "use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So we must use those exact heading names? It says "e.g." meaning examples, but likely we should use those headings where appropriate. We need to include a section only when posting has real specific content for it. So we need to map content to these categories. We have responsibilities: many bullet points describing duties. That fits "Responsibilities". Requirements/Qualifications/Skills: experience and qualifications section. Benefits: not mentioned except maybe salary range? Salary is Pay. Benefits not given. Pay: salary range given. Schedule: not mentioned. About the role: could be a brief summary. We could include About the role as a short paragraph summarizing the role. We must not repeat job title as heading. No Job ID. We must remove boilerplate etc. The posting includes no obvious boilerplate besides maybe the list of measurement etc. We'll keep substantive info. We need to use

• for any list of duties/requirements/benefits. So for responsibilities we should list each bullet as
• . For qualifications we can list each bullet as
• . For skills maybe also list. We need to preserve ALL substantive information; do not invent or summarize away detail. So we need to keep all bullet points exactly as given? We can keep the text but maybe rephrase? Must not summarize away detail; we can keep them as is. We need to output only HTML fragment, no markdown fences. Let's extract sections: About the role: maybe a brief intro: "AI Fluency and Agentic Delivery Use AI development tools personally and daily..." Actually that's first bullet under responsibilities. Could treat that as responsibilities. But we need an About the role paragraph summarizing the role: maybe combine first few sentences? However we must not invent; we can take the first paragraph before bullet list? The posting starts with "AI Fluency and Agentic Delivery Use AI development tools personally and daily, holding a current and practical command of what they can and cannot do rather than relying on secondhand reporting." That's actually a responsibility bullet. There's no separate overview. We could still create an About the role section with a short paragraph summarizing the role: maybe combine the first sentence of each major heading? But that might be summarizing. Safer: we could omit About the role if no explicit overview. But instruction: "Prefer a few substantive sections over many thin ones." We could include About the role with a generic summary derived from the posting, but that might be considered summarizing. However we need to include a section only when posting has real specific content for it. If we create a summary, that's not directly from posting; it's our own. Might be considered inventing. Better to omit About the role if no explicit overview. But we need at least one section? Not required. We can output multiple sections. Let's include Responsibilities, Qualifications (or Requirements), Skills, Pay. We need to decide which headings map to which content. Responsibilities: all bullet points under "AI Fluency and Agentic Delivery", "Technical Roadmap and AI-Driven Opportunity", "Delivery and Execution", "Product Quality and Security", "Team Leadership and Development", "Financial and Resource Ownership", "Platform Modernization", "Stakeholder and Executive Communication". That's many. Qualifications/Requirements/Skills: "Experience And Qualifications" section includes bullet points about degree, years, AI usage, etc. Also "Performance Measurement Success In This Role Is Measured On The Following" includes metrics; that could be considered part of responsibilities? Actually it's how success measured; could be considered part of responsibilities or maybe a separate section like "Performance Metrics". But not in allowed headings. Could fold into Responsibilities or Qualifications? Probably better to include under Responsibilities as they describe expectations. But we could also create a separate section "Requirements" for the experience and qualifications. Skills maybe from the qualifications bullet points about hands-on use, understanding, record, etc. Pay: salary range. We must not create Benefits or Schedule sections if not present. Thus output:

  Responsibilities

  • ... each bullet ...

  Qualifications

  • ... each bullet from Experience And Qualifications ...

  Skills

  • ... maybe extract skill-related bullets from qualifications? But we might duplicate. Better to keep Qualifications only and not Skills if overlapping. But we need to avoid thin sections. Could combine Qualifications and Skills into one section called "Qualifications". That's fine. Thus we have Responsibilities and Qualifications and Pay. Pay:

    Pay

    Salary range: $125,000 - $135,000 USD per year.

    We must not include extra fluff. Now we need to ensure we preserve all substantive information. Let's list all bullet points from responsibilities sections. I'll copy them: From "AI Fluency and Agentic Delivery": - Use AI development tools personally and daily, holding a current and practical command of what they can and cannot do rather than relying on secondhand reporting. - Research and experiment continuously with new AI tooling, models, and workflows, then bring what proves out into the organization's standard practice. - Lead every development team to agentic, human-on-the-loop delivery, where AI agents own bounded work end to end while engineers set goals, define guardrails, and review outcomes. - Own the AI delivery-maturity roadmap across all actively supported products, applied stage by stage across the lifecycle rather than as a single global rating. - Stand up the governance that makes delegated agent work safe, including review gates, validation, security review of AI-generated code, and measurable outcome reporting. - Make the codebase and delivery pipeline accessible to AI agents through documentation, test coverage, CI/CD, and structured context. - Build the tooling and training program that moves engineers from AI-assisted work, to AI-directed work, to fully delegated agent execution. - Report AI delivery maturity and the business outcomes it produces on a recurring executive cadence. From "Technical Roadmap and AI-Driven Opportunity": - Set the multi-year technical roadmap for the Corrections and Law Enforcement product lines, planned against the pace agentic delivery now makes possible rather than the timelines conventional development assumed. - Rebuild roadmap assumptions around that acceleration. - Pull committed work forward where AI-enabled delivery shortens the build, and direct the recovered capacity toward customer problems that were previously out of reach. - Plan the roadmap around customer problems as much as product features, judging each item on whether AI can reach the outcome faster than a conventional build cycle would. - Decide deliberately where AI belongs embedded in the products customers buy and where it is better applied as a fast path to a customer outcome, and treat both as revenue opportunities rather than internal efficiency alone. - Balance that acceleration against customer commitments already in flight and the realities of an installed base that cannot absorb change at an unlimited rate. - Own technology standards, architectural direction, and technical documentation across the portfolio, and hold the development organizations to them. - Evaluate build, buy, and consolidation options where product lines overlap, and bring recommendations with supporting analysis to the Executive Vice President. - Monitor industry direction, competitor capability, and emerging technology relevant to law enforcement, corrections, and justice software. From "Delivery and Execution": - Own release commitments across every development team, including scope, sequencing, and the dates communicated to customers and to sales. - Establish a single reporting cadence for status, blockers, and slips so that risk surfaces early rather than at the release gate. - Standardize development process, tooling, and engineering metrics across companies that today operate independently. - Resolve cross-team dependencies and resource contention, and escalate the trade-offs that require an executive decision. From "Product Quality and Security": - Set and enforce quality standards covering code review, automated testing, defect thresholds, and release readiness, applied equally to human-written and AI-generated code. - Maintain the highest levels of product and platform security, promoting a culture and practice of security awareness in every development team. - Ensure development activities meet the regulatory, contractual, and industry requirements that apply to public safety and justice customers. - Partner with Support Services to close the loop between escalated customer issues and engineering priorities. From "Team Leadership and Development": - Lead, mentor, and develop the development managers and directors across the portfolio companies, setting clear goals and performance expectations. - Recruit and retain engineering talent, and build succession depth in every key technical role. - Set the expectation that every engineer works with AI tools as a normal part of the job, and give them the training, access, and time to get there. - Structure the organization for the work ahead, including the balance between onshore, offshore, and contract capacity, and the reshaping that agentic delivery makes possible. - Promote transparency and collaboration by making priorities, progress, and decisions visible across the organization. From "Financial and Resource Ownership": - Own the R&D budget across the portfolio, including headcount planning, capitalization, AI tooling spend, and vendor commitments. - Contribute R&D inputs to the monthly forecast and to quarterly executive reporting, with explanations for variance against plan. - Drive measurable improvement in development cost as a percentage of revenue without sacrificing delivery or quality. - Allocate engineering capacity to the products and initiatives with the strongest return, and defend those choices with data. From "Platform Modernization": - Lead platform migration and modernization programs, including the sequencing and customer impact of each phase. - Retire technical debt and legacy dependencies on a published schedule rather than opportunistically, recognizing that a cleaner codebase is also a more AI-accessible one. - Measure adoption and business impact of new capability, and stop investment that does not earn its place. From "Stakeholder and Executive Communication": - Act as the primary liaison between R&D and Operations, Support, Product Management, Sales, and Finance. - Present roadmap progress, AI maturity, risk, and investment recommendations to executive leadership in terms a non-technical audience can act on. - Engage directly with customers, partners, and user groups to validate direction and to hear where the products fall short. - Support due diligence and technical integration for acquisitions that join the portfolio. Now qualifications bullets from "Experience And Qualifications": - Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field; advanced degree preferred. - Ten or more years in software development with at least three years leading engineering organizations at director level or above. - Demonstrated hands-on use of AI development tools in daily work, with a clear point of view on where they help, where they fail, and how quickly that is changing. - Experience raising an engineering organization's way of working from individual AI assistance to agentic, human-on-the-loop delivery, including the governance and validation that makes it safe. - Strong understanding of software architecture, cloud technologies, modern development methodologies, and enterprise software delivery. - A record of translating AI capability into customer-facing value and revenue, not only internal development efficiency. - Proven ownership of a multi-product or multi-company development portfolio, including budget accountability. - Experience delivering enterprise software to public sector, public safety, or other regulated markets is strongly preferred. - Track record modernizing legacy platforms while continuing to support an installed customer base. - Working command of modern architecture, cloud delivery, and secure development practice, including