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About the Role

This role leads the technical roadmap and AI-driven opportunity for Corrections and Law Enforcement product lines. It involves setting multi-year technical roadmaps, rebuilding assumptions around AI-enabled delivery acceleration, and planning roadmaps around customer problems. The role also involves deciding where AI should be embedded in products and balancing acceleration against customer commitments and installed base realities. Additionally, it requires owning technology standards, architectural direction, and technical documentation, as well as evaluating build, buy, and consolidation options.

Responsibilities

Use AI development tools personally and daily, holding a current and practical command of what they can and cannot do.
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.
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.
Set the multi-year technical roadmap for the Corrections and Law Enforcement product lines, planned against the pace agentic delivery now makes possible.
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.
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.
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.
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.
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.
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.
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.
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.
Measure adoption and business impact of new capability, and stop investment that does not earn its place.
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.

Requirements

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.

Skills

AI development tools
AI tooling
AI models
AI workflows
Agentic delivery
Human-on-the-loop delivery
AI agents
AI delivery-maturity roadmap
Governance
Validation
Security review of AI-generated code
AI-accessible codebase
AI-accessible delivery pipeline
AI-assisted work
AI-directed work
Delegated agent execution
Technical roadmap
AI-enabled delivery
Customer problems
Product features
Revenue opportunities
Technology standards
Architectural direction
Technical documentation
Industry direction
Competitor capability
Emerging technology
Release commitments
Development process standardization
Tooling standardization
Engineering metrics standardization
Cross-team dependency resolution
Resource contention resolution
Quality standards enforcement
Code review
Automated testing
Defect thresholds
Release readiness
Product security
Platform security
Security awareness
Regulatory requirements
Contractual requirements
Industry requirements
Customer issue resolution
Team leadership
Mentoring
Talent recruitment
Talent retention
Succession planning
Engineering talent development
AI tools training
Organizational structuring
Onshore capacity
Offshore capacity
Contract capacity
Transparency
Collaboration
R&D budget ownership
Headcount planning
Capitalization
AI tooling spend
Vendor commitments
Financial forecasting
Executive reporting
Development cost management
Engineering capacity allocation
Platform modernization
Platform migration
Technical debt retirement
Legacy dependency retirement
Stakeholder communication
Executive communication
Customer engagement
Partner engagement
User group engagement
Due diligence support
Technical integration support
Software architecture
Cloud technologies
Modern development methodologies
Enterprise software delivery
Customer-facing value creation
AI capability translation
Multi-product portfolio ownership
Multi-company portfolio ownership
Budget accountability
Enterprise software delivery to public sector
Enterprise software delivery to public safety
Enterprise software delivery to regulated markets
Legacy platform modernization
Installed customer base support
Modern architecture
Cloud delivery
Secure development practice
Security review of AI-generated code

Experience Level

Ten or more years in software development
At least three years leading engineering organizations at director level or above

Education Level

Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field
Advanced degree preferred
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience

Salary/Compensations

$125,000 - $135,000 USD per year