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The Application Development Manager leads a cross-functional team of developers, business analysts, and QA analysts responsible for building, integrating, and supporting the applications that run Stanley Steemer's corporate, franchise, and field operations. This role owns delivery for a portfolio that spans a modern .NET platform and a decades-old legacy system of record and is accountable for both the throughput of the team and the operational health of what it ships.

This position carries a second mandate: leading the team's transition from a traditional SDLC to an AI-first, spec-driven development model. Stanley Steemer is actively moving toward a way of working where the durable artifact is a well-written specification, AI coding agents do a large share of the implementation, and the team spends its time on problem definition, architecture, review, and verification. We are looking for a manager who has either done this or is genuinely eager to lead it - not one who intends to wait it out.

This is a hands-on people-leadership role. The manager must be technically credible enough to review a design, judge whether a specification is good enough to hand to an agent, sit in on a production incident, and challenge an estimate - while spending the majority of their time on people, planning, prioritization, and stakeholder alignment rather than writing production code themselves.

The team owns a portfolio of business-critical internal applications, including:

• Field operations platforms - call center order-taking and dispatch systems, and mobile applications used daily by technicians in the field for scheduling, job status, pricing, and payment collection
• Line-of-business web portals - internal applications used by corporate staff, company-owned branches, and independent franchisees to manage customers, employees, fleet, inventory, and finance
• Legacy modernization - a multi-year, feature-by-feature migration of a legacy system of record onto the modern platform, executed without disrupting the operations that depend on it
• Enterprise system integrations - two-way interfaces between internal systems and third-party SaaS platforms for CRM, ERP and commerce, HR and payroll, learning management, telematics, and payment processing
• Background processing and services - a portfolio of APIs, message-driven workers, and scheduled jobs supporting the above
• Shared platform components - internal libraries, messaging infrastructure, and an emerging set of internal services that give AI development tooling governed access to company systems and documentation

The environment is genuinely mixed: some applications are current-generation .NET, others run on frameworks approaching end of life, and several depend on data stores and technologies that predate them by decades. Comfort operating across that range is essential.

• Ownership - treats production stability as their responsibility, not the vendor's, the agent's, or the previous manager's.
• Precision in writing - can turn a vague business request into a specification another person or an agent can execute without guessing.
• Calibrated trust in tooling - neither dismisses AI output nor accepts it unverified; knows the difference between reviewed and merely generated.
• Multi-disciplinary leadership - develops analysts and testers as first-class contributors, not as support staff for developers.
• Pragmatism - chooses the change the business can absorb over the architecturally ideal one and knows when that trade goes the other way.
• Candor - surfaces bad news early, with a recommendation attached. Including bad news about the AI transition itself.
• Team advocacy - protects the team's focus and represents its constraints honestly upward.