Vice President of Research & Development

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. The posting includes many bullet points under responsibilities, requirements, qualifications, etc. Also includes Salary range. No explicit Benefits, Schedule. So we should include sections: About the role (maybe a brief paragraph summarizing role?), Responsibilities, Requirements, Qualifications, Skills, Pay. Possibly also "Experience And Qualifications" but that is similar to Qualifications. We need to parse the posting. Let's extract content: The posting starts with a list of bullet points describing responsibilities and expectations: - AI Fluency and Agentic Delivery (multiple bullet points) - Technical Roadmap and AI-Driven Opportunity (multiple bullet points) - Delivery and Execution (multiple bullet points) - Product Quality and Security (multiple bullet points) - Team Leadership and Development (multiple bullet points) - Financial and Resource Ownership (multiple bullet points) - Platform Modernization (multiple bullet points) - Stakeholder and Executive Communication (multiple bullet points) Then "Experience And Qualifications" section with bullet points. Then "Performance Measurement Success In This Role Is Measured On The Following" list. Then "Salary range: $125,000 - $135,000 USD per year." We need to map to sections. We can treat "About the role" as a brief paragraph summarizing the role: maybe combine the introductory sentence? The posting starts with "AI Fluency and Agentic Delivery Use AI development tools personally..." but that's actually a heading within responsibilities. There's no explicit "About the role" paragraph. However we could create a short paragraph summarizing: The role is Director of Engineering focusing on AI-driven agentic delivery, roadmap, etc. But we must not invent detail. We can maybe omit About the role if no specific content. The instruction: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text. So if we don't have a distinct "About the role" paragraph, we should not include that heading. Thus we can include sections: Responsibilities, Requirements, Qualifications, Skills, Pay. Possibly also "Performance Measurement" could be considered as "Success Metrics" but not in allowed headings list. The allowed headings are About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We should stick to those. So we need to map content to these. Responsibilities: all the bullet points under the headings AI Fluency..., Technical Roadmap..., Delivery..., Product Quality..., Team Leadership..., Financial..., Platform..., Stakeholder... Those are responsibilities. Requirements: maybe the "Experience And Qualifications" section includes requirements like degree, years experience, AI fluency, etc. Could be split into Requirements and Qualifications. But we can treat "Experience And Qualifications" as Qualifications. Requirements could be things like "Bachelor's degree..." etc. However the posting lumps them together. We could put all under Qualifications. Or we could split: Requirements: Bachelor's degree, 10+ years experience, etc. Qualifications: demonstrated hands-on use, experience raising org's way of working, etc. But we need to avoid duplication. Better: Use Requirements for the hard requirements: degree, years experience, etc. Use Qualifications for the demonstrated experience, track record, etc. Skills could be things like understanding of software architecture, cloud technologies, modern dev methodologies, enterprise software delivery, etc. Also "Working command of modern architecture, cloud delivery, and secure development practice, including security review of AI-generated code." That's skill. Let's extract: Experience And Qualifications bullet points: - 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 security review of AI-generated code. - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience. (duplicate of first?) Note duplicate. We can categorize: Requirements (hard): - Bachelor's degree in CS, Engineering, IT, or related; advanced degree preferred. - Ten+ years software development, with at least 3 years leading engineering orgs at director level or above. - Bachelor's degree in CS, Engineering, or related field, or equivalent professional experience (maybe redundant). Qualifications (demonstrated): - Demonstrated hands-on use of AI development tools daily, with clear POV. - Experience raising org's way of working from AI assistance to agentic, human-on-the-loop delivery, including governance. - Strong understanding of software architecture, cloud tech, modern dev methodologies, enterprise software delivery. - Record of translating AI capability into customer-facing value and revenue. - Proven ownership of multi-product/multi-company dev portfolio, budget accountability. - Experience delivering enterprise software to public sector, public safety, regulated markets (strongly preferred). - Track record modernizing legacy platforms while supporting installed base. - Working command of modern architecture, cloud delivery, secure dev practice, including security review of AI-generated code. Skills could be: understanding of software architecture, cloud technologies, modern development methodologies, enterprise software delivery, etc. But that overlaps with qualifications. Maybe we treat Skills as: Strong understanding of software architecture, cloud technologies, modern development methodologies, enterprise software delivery; Working command of modern architecture, cloud delivery, secure development practice; etc. But we need to avoid duplication. Perhaps we can put all under Qualifications and skip Skills. However we need to follow instruction: Prefer a few substantive sections over many thin ones. So we could have Responsibilities, Qualifications, Pay. That's three sections. That seems acceptable. But we need to include Requirements if we have content. We could combine Requirements and Qualifications into one section called Requirements (or Qualifications). The allowed headings include both; we can choose to include only those with content. If we put all under Qualifications, we need to ensure we don't miss any content. Let's see if any content fits better as Requirements: the degree and years experience are typical requirements. We could put those under Requirements, and the rest under Qualifications. That yields two sections plus Responsibilities and Pay. Thus sections: Responsibilities, Requirements, Qualifications, Pay. Now we need to extract responsibilities content. Responsibilities bullet points grouped under headings: 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. 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. 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. 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. 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. 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. 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. 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,