研发副总裁

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, 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.

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 security review of AI-generated code.

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.

Performance Measurement
Success in this role is measured on the following:

• AI delivery maturity: Teams advancing to agentic, human-on-the-loop delivery on the published stage-by-stage plan

• Roadmap acceleration: Committed work delivered ahead of conventional timelines, with the recovered capacity visibly redeployed

• AI-driven revenue: Customer problems solved and revenue generated through AI capability, whether embedded in the products or applied as a fast path to an outcome

• Release predictability: Committed releases delivered on the dates given to customers and to sales

• Product quality: Escaped defect and escalation volume trending down release over release

• Financial performance: R&D spend held to plan, with development cost as a percentage of revenue improving

• Organizational health: Retention of key engineering talent and depth in every critical technical role

• Cross-functional standing: Support, Sales, and Operations report a working, transparent relationship with R&D

Salary range: $125,000 - $135,000 USD per year.
Originally posted on Himalayas