Manager, Development

The Manager, Development leads the strategic direction and operational execution of multiple software development teams, typically managing a one-to-three-year roadmap horizon. In this role, you collaborate closely with cross-functional partners to deliver critical software initiatives, ensuring continuous alignment with organizational objectives. You actively foster team development, optimize productivity, and promote an agile, collaborative environment. Through strategic leadership and effective communication, you drive technical excellence, innovation, and a culture of continuous improvement. You lead AI-First teams, where AI agents are the default way work is planned, built, tested, and reviewed, and you are accountable for doing so with the verification, governance, and measurement discipline our regulated environment requires. 

Job Responsibilities  

• Team Leadership & Management: Attracts, hires, coaches, and retains high-performing software development engineers, actively developing personalized career plans that include AI fluency progression, and promptly addressing performance issues. 

• Accountable Delivery: Effectively plans, manages, and leads the delivery of roadmap items, including product and modernization initiatives, leveraging AI-driven workflows to compress cycle time while holding quality and reliability constant. 

• Organizational Alignment: Maintains a 360-degree alignment between product roadmap, architectural strategy, and software development processes to ensure the delivery of value-generating software solutions. 

• Collaboration: Works proactively with cross-functional partners (product management, architecture, QA, operations) to clearly define strategic goals and maintain transparency in planning, execution, and delivery. 

• Architecture & Technology Strategy: Actively contributes to shaping and evolving the architectural and development strategies, aligning closely with product roadmaps, ensuring scalability, maintainability, and quality. 

• Quality and Continuous Improvement: Promotes a culture of quality and continuous improvement by implementing Agile and Lean Development methodologies in collaboration with cross-functional partners, fostering a shift-left mindset, holding AI-generated code to the same test coverage, review, and security standards as human-authored code, and ensuring consistent developer engagement and productivity improvements. 

• Customer Experience: Delivers excellent customer experiences by ensuring high-quality, reliable software products. 

• Scalability: Participates in defining architectural direction and development strategies to ensure scalable and maintainable software solutions. 

• Security: Provides leadership in application security remediation activities and mentors on implementing security remediation and secure programming practices; ensures secure use of AI tooling, including approved tools only, least-privilege agent permissions, no sensitive or customer data in unapproved systems, and a full audit trail for agent-made changes. 

• Technical Contribution: Contributes technically when needed, including directing and reviewing agentic work, and maintains strong hands-on engagement with the team’s AI toolchain to support the team and organizational outcomes. 

• Technology Innovation: Actively contributes to evolving the organization's core technology stack through innovative adoption and enhancement of cloud, web, mobile, microservices, big data, and API-enabled platforms. 

• AI-First Delivery: Establishes AI-assisted development as the team’s default operating model, with spec-driven intent, agent-executed implementation, and human-verified outcomes, and measurably raises the team’s AI maturity each quarter by tracking utilization, cycle-time impact, and cost per delivered outcome. 

• AI-Enabled Product Delivery: Partners with Product and Architecture to identify, scope, and ship AI-powered capabilities in our products, applying guardrails for accuracy, explainability, data privacy, and model risk. 

• Responsible AI & Governance: Ensures team adherence to the organization’s AI stance and enterprise risk expectations, including approved tools, permission tiers, human-in-the-loop gates, and provenance for agent-involved changes, and holds engineers accountable for reviewing every AI-generated change for intent, risk, and correctness. 

• AI Capability Building: Coaches engineers from AI-assisted coding toward directing agents, developing skills in decomposition, specification, context engineering, and verification; owns the team’s version-controlled AI context assets (agent instructions, skills, prompts, and configurations); and develops champions to accelerate peer adoption. 

• Talent Pipeline: Actively invests in co-op programs and consistently identifies, cultivates, and converts suitable candidates into full-time software engineers. 

Requirements  

• B.S. in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience). 

• 5+ years of software development experience, demonstrating strong proficiency in software engineering. 

• Proven track record in technical leadership roles, with 1+ years managing and leading engineering teams. 

• Extensive hands-on experience in software engineering practices and successfully leading engineering teams to deliver impactful outcomes. 

• Strong expertise working within Agile/Lean environments, including practical experience with common Agile/Lean frameworks (e.g., Scrum, Kanban). 

• Demonstrated ability to attract, hire, develop, and retain high-quality technical talent, including experience managing globally distributed or remote engineering teams. 

• Exceptional communication skills, capable of clearly conveying technical information and strategies to diverse stakeholders. 

• Hands-on experience using AI coding agents in production software delivery, including spec-driven delegation and review of agent-generated changes. 

• Demonstrated experience leading a team through AI tooling adoption, including changes to workflow, review practices, and definition of done. 

• Working understanding of AI risk in regulated environments, including data handling, model limitations, auditability, and human oversight requirements.