Vice President of Engineering

Engineering Leadership and Team Building

• Build, lead, and scale a high-performing Engineering organization across U.S.-based, nearshore, and offshore teams.
• Define the Engineering operating model, organizational structure, ownership boundaries, technical leadership model, and delivery expectations.
• Recruit, develop, and retain Engineering leaders, architects, full-stack engineers, QA professionals, DevOps resources, and technical leads.
• Create a culture of accountability, urgency, collaboration, craftsmanship, technical excellence, and continuous improvement.
• Establish clear performance expectations, career paths, Engineering standards, team health metrics, and leadership development plans.

Engineering Scale and Operating Model

• Design team structures that promote focus, accountability, technical depth, and scalable product development.
• Build an effective blend of employees and delivery partners to increase capacity, flexibility, and execution speed.
• Establish strong onboarding, documentation, knowledge-sharing, code ownership, architecture review, and technical readiness practices.
• Create repeatable processes for planning, delivery, support, technical decisions, and cross-functional coordination.
• Partner with Product, Customer Success, Sales, Operations, Finance, HR, and executive leadership to align resources with company priorities.

AI-Enabled Engineering and SDLC Modernization

• Design and implement an AI-enabled software development lifecycle that improves productivity, quality, speed, and developer experience.
• Operationalize AI-assisted tools for coding, testing, documentation, code review, refactoring, debugging, requirements interpretation, release readiness, and production support.
• Integrate human technical judgment and AI acceleration across planning, architecture, development, testing, deployment, monitoring, and incident response.
• Establish responsible AI development standards covering security, intellectual property, privacy, code review, test coverage, architectural consistency, and human accountability.
• Partner with Product and UX to structure requirements, designs, and acceptance criteria for effective AI-assisted development.
• Develop internal Engineering agents, accelerators, automation, and workflows that reduce manual work and improve throughput.

AI-Powered Product and Platform Development

• Provide technical leadership for AI-powered product capabilities, platform services, internal tools, and customer experiences.
• Evaluate opportunities involving AI, automation, agents, LLM integrations, personalization, recommendations, content generation, and intelligent workflows.
• Partner with Product Management to assess feasibility, architecture, cost, scalability, risk, and customer value.
• Guide the architecture of AI capabilities, including model integrations, prompt orchestration, data pipelines, APIs, permissions, observability, auditability, and human-in-the-loop workflows.
• Ensure AI features meet appropriate quality, security, privacy, compliance, monitoring, explainability, and governance standards.
• Help move the company from AI experimentation to repeatable, production-grade capabilities.

Delivery Excellence, Speed, and Quality

• Drive consistent, predictable, high-quality delivery across teams and initiatives.
• Establish operating rhythms for planning, estimation, dependency management, technical reviews, release readiness, and executive reporting.
• Implement automation-first practices across CI/CD, testing, QA, observability, security scanning, infrastructure provisioning, deployment, and release management.
• Improve speed by reducing rework, strengthening requirements readiness and technical design, and creating clear ownership.
• Define and track metrics such as cycle time, deployment frequency, lead time, defect escape rate, test coverage, production incidents, reliability, capacity, and release predictability.
• Establish clear definitions of ready and done, automated quality gates, disciplined release practices, and measurable success criteria.
• Create rapid feedback loops using customer input, production usage, support issues, incidents, and post-release data.

Technical Strategy and Architecture

• Translate company and product strategy into a scalable technical strategy and execution roadmap.
• Lead architecture, platform scalability, reliability, security, performance, data, APIs, integrations, DevOps, AI enablement, and technical debt management.
• Align Engineering investments with business outcomes, revenue priorities, customer commitments, enterprise readiness, and long-term platform health.
• Establish architecture review practices that support speed, consistency, extensibility, maintainability, security, and resilience.
• Balance near-term delivery priorities with long-term technical sustainability.

Operational Discipline and Risk Management

• Create visibility into Engineering capacity, allocation, delivery status, dependencies, risks, and tradeoffs.
• Manage budgets, hiring plans, contractor spend, vendors, software tooling, infrastructure costs, and resource allocation.
• Identify and mitigate operational, security, scalability, architectural, delivery, personnel, compliance, and platform risks.
• Establish effective incident management, production support, monitoring, alerting, reliability, and postmortem practices.
• Ensure Engineering processes support security, privacy, compliance, auditability, enterprise.

Player-Coach Leadership

• Contribute directly to architecture, technical design, prototyping, troubleshooting, and implementation when needed.
• Guide critical technical decisions, architecture proposals, design patterns, code quality standards, and Engineering tradeoffs.
• Serve as an escalation point for complex platform, scalability, reliability, performance, integration, data, and security challenges.
• Work directly with engineers to unblock delivery and model strong Engineering practices.