Software Engineering Manager (AI-Enabled)

IntelliTrans•Dunwoody, GA

11h•Hybrid

About The Position

IntelliTrans (ITL), a subsidiary of Roper Technologies, is seeking an AI Enabled Engineering Manager to join their team, hybrid in Atlanta, GA. The Engineering Manager will lead cross-functional SaaS engineering teams and serve as a transformational leader in how IntelliTrans designs, builds, and delivers software. This role owns the full software development lifecycle (SDLC) across multiple delivery teams and is accountable for Agile execution, engineering quality, release velocity, and team development. The ideal candidate is an entrepreneurial engineering leader who has moved from hands-on development into management and brings a modern perspective on AI-driven development, including active use of AI-assisted coding tools such as Claude Code, GitHub Copilot, or similar. The position partners closely with the CTO, Chief Architect, and product leadership to align engineering execution with company strategy and customer outcome.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or related field
7+ years of software engineering experience, with at least 3 years in an engineering management or team lead role
Demonstrated experience leading SaaS product engineering teams in a fast-paced, Agile environment
Hands-on background as a software developer prior to moving into management (ideally full-stack or backend)
Proven experience owning and improving the full SDLC, from story refinement through production deployment
Practical experience with AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, or equivalent) and a track record of driving their adoption
Strong command of Agile/Scrum delivery at the team level and experience with scaled frameworks (SAFe, Lean Startup, or similar)
Experience leading transformational engineering initiatives that delivered measurable business or operational impact
Track record of recruiting, developing, and retaining strong engineering talent
Lean Startup, Agile/Scrum delivery, SAFe or similar scaled Agile program management
Full SDLC ownership across planning, development, testing, release, and operations
Engineering metrics: DORA metrics, cycle time, deployment frequency, change failure rate
Technical roadmap planning and cross-squad delivery coordination
Backlog management, sprint planning, and program increment execution
AI code generation tools: Claude Code, GitHub Copilot, Cursor, Amazon CodeWhisperer
AI-augmented testing, AI-assisted code review, and prompt engineering for development tasks
SDLC transformation to incorporate AI tooling at each lifecycle stage
Responsible AI practices: review workflows, quality gates, and governance for AI-generated code
Hands-on software development background (any major stack; ideally one or more of: .NET Core, Java, Angular, Flutter/Dart)
RESTful API design, microservices architecture, and cloud-native development patterns
CI/CD pipeline design and deployment automation (GitLab CI/CD, Jenkins, or similar)
Source control strategy: GitFlow, trunk-based development, branching and release strategies
Code quality practices: peer review, static analysis, test automation, and coverage targets
Test strategy across unit, integration, end-to-end, and performance testing
Test automation frameworks and shift-left quality practices
Defect lifecycle management, regression strategy, and release quality gates
AWS core services (compute, storage, networking, managed databases)
Container basics (Docker, ECS/EKS) and cloud-native deployment patterns
Observability fundamentals: logging, monitoring, alerting, and incident response
Entrepreneurial mindset with a bias for action, ownership, and driving meaningful change
Ability to lead transformational initiatives end-to-end, from vision through sustained adoption
Strong executive presence and ability to represent engineering to senior leadership and external stakeholders
Strategic thinking balanced with operational discipline and delivery accountability
Experience identifying and closing capability gaps through hiring, training, and process improvement
Genuine enthusiasm for AI-assisted development and a track record of enabling teams to work smarter with AI tools
Curiosity about emerging engineering tools, workflows, and development paradigms
Ability to evaluate new technology pragmatically and drive adoption at scale
Growth mindset and commitment to continuous learning in a rapidly evolving technical landscape
Coaching and mentoring approach that develops both technical skills and leadership capacity in others
Inclusive leadership style that fosters psychological safety, collaboration, and high performance
Ability to deliver candid feedback constructively and navigate performance conversations with care
Strong hiring instincts and ability to build diverse, high-performing engineering teams
Excellent written and verbal communication skills; able to make complex technical topics accessible to non-technical audiences
Skilled at working across functional boundaries with product, design, architecture, and operations partners
Transparent, direct communicator who keeps stakeholders informed and manages expectations proactively
Effective in cross-functional forums, leadership reviews, and customer-facing escalation situations
Data-driven approach to delivery decisions, quality investments, and team capacity planning
Ability to balance technical excellence with business pragmatism and delivery urgency
Sound judgment in navigating technical debt, scope trade-offs, and competing priorities
Root-cause orientation with a drive to implement systemic improvements rather than workarounds
Self-motivated with strong ownership, follow-through, and accountability
Organized and structured approach to managing multiple teams, initiatives, and priorities simultaneously
Commitment to engineering craft: quality, reliability, maintainability, and security
Passion for building great products and great teams

Nice To Haves

Master's degree in Computer Science, Software Engineering, or Business Administration
Experience leading or closely partnering with QA, Cloud Ops, or IT functions
Familiarity with Program Increment (PI) planning and large-scale Agile coordination
Experience in a growth-stage or entrepreneurial SaaS company, including navigating rapid change and resource constraints
Exposure to AI/ML-enabled product features or data-intensive SaaS platforms
AWS architecture awareness or cloud engineering background
Experience building or managing offshore or distributed engineering teams
Databricks (a plus)

Responsibilities

Lead and develop multiple software engineering teams across the full SDLC, from planning through deployment and continuous improvement
Drive Agile/Scrum delivery, sprint cadence, team accountability, and engineering best practices across all squads
Own program-level delivery planning and cross-team coordination using SAFe, Scrum, or comparable Lean or Agile frameworks
Establish and enforce engineering standards, coding practices, code review culture, and quality gates
Set and track key delivery metrics (cycle time, deployment frequency, defect escape rate, lead time) and drive continuous improvement
Oversee release planning, version management, and coordinated deployment across environments
Champion the adoption of AI-driven (not just AI-assisted) development workflows, including AI code generation, AI-augmented testing, and AI-driven code review
Drive hands-on enablement of AI coding tools (Claude Code, GitHub Copilot, Cursor, or similar) across engineering teams
Lead transformation of the SDLC to incorporate AI at every stage: requirements, design, implementation, testing, and operations
Evaluate, pilot, and scale emerging AI development tooling to improve team productivity and code quality
Build a culture of responsible AI use, including code review practices for AI-generated output, prompt engineering literacy, continuous learning, innovation, risk mitigation, and ROI management
Recruit, mentor, and develop engineering talent, building a high-performing, collaborative, and psychologically safe team culture
Conduct regular 1:1s, performance reviews, and growth planning conversations with direct reports and team leads
Foster an entrepreneurial mindset within teams, encouraging ownership, experimentation, and proactive problem-solving
Build a culture of accountability, continuous learning, and delivery excellence
Identify organizational gaps and lead hiring efforts to scale the engineering team strategically
Identify and lead transformational engineering initiatives that meaningfully improve delivery speed, system quality, or operational efficiency
Partner with the CTO and Chief Architect on strategic roadmap execution and technical modernization efforts
Champion process improvements, toolchain upgrades, and workflow automation to reduce toil and increase developer productivity
Lead change management for new engineering practices, tools, and ways of working across the organization
Partner closely with Product Management to translate business and customer requirements into executable engineering plans
Collaborate with the Chief Architect and platform teams on architecture decisions, technical debt prioritization, and system design
Work with QA, Cloud Ops, and IT leadership to ensure integrated delivery, operational readiness, and cross-team alignment
Represent engineering in leadership forums, customer escalations, and strategic planning discussions
Build effective relationships with stakeholders across the business to ensure engineering execution aligns with company goals
Drive engineering quality through testing strategy, automation coverage goals, and shift-left quality practices
Oversee incident response, post-mortems, and root cause analysis for production issues affecting product delivery
Ensure compliance with security, privacy, and software governance requirements across all engineering output
Manage engineering tools, licenses, and resource allocation in alignment with budget expectations