Vice President of Engineering

SundaySky; United States; On-site; USD 260,000 - 280,000; Full time

Benefits offered by this job

Annual bonus

Equity

401(k) plan

Medical insurance

Dental insurance

Vision insurance

Short-term disability

Paid parental leave

Job summary

SundaySky is seeking a deeply technical, AI-native engineering leader to define our architecture, transform how software is built, and lead a focused team of highly experienced engineers. This is not a traditional management-first role; it is hands-on, building architecture and implementing core systems.

You will own architecture across applications, data, cloud, and AI, prototype in code, and drive measurable improvements.

Qualifications

• Hands-on software engineering experience with architecture, implementation, and production problem-solving.
• Deep architectural expertise across frontend/backend, APIs, cloud, security, observability, and reliability.
• Experience using AI coding agents to build, test, refactor, debug, or maintain production software.
• Ability to design AI-enabled development workflows with human direction, review, and accountability.
• Proven track record of leading engineers and delivering complex initiatives.

Responsibilities

• Own SundaySky's application, platform, data, integration, cloud, and AI architecture.
• Work directly in the codebase with prototyping, implementation, and code review.
• Translate product strategy into pragmatic designs and executable plans.
• Create reference architectures, patterns, and standards to accelerate development.
• Lead modernization while preserving stability and customer commitments.
• Establish delivery discipline and ownership with a small, senior U.S.-based team.

Skills

Hands-on coding

Architectural expertise

AI coding agents

Technical leadership

Principal engineer

Cloud-native architecture

Agent orchestration

Code review

Security & reliability

CI/CD / automation

Tools

GitHub Copilot

Claude Code

Codex

Cursor

Job description

SundaySky is seeking a deeply technical, AI-native engineering leader to define our architecture, transform how software is built, and lead a focused team of highly experienced engineers. This is not a traditional, management-first Vice President of Engineering role. We are not looking for an executive who has moved away from software development or whose primary strength is managing managers. We are looking for an exceptional architect, distinguished engineer, or hands-on Engineering Director who is ready to assume broader ownership for an Engineering organization while remaining directly involved in architecture and implementation. Our Engineering model is intentionally different. We believe a smaller team of accomplished senior engineers-equipped with well-designed AI agents, automation, and disciplined technical oversight-can outperform a much larger traditional development organization. In this model, AI performs much of the implementation work typically assigned to junior developers, while experienced engineers provide architecture, context, judgment, review, and accountability. The successful candidate will personally help build this model. They will work in the codebase, develop reference implementations, establish architectural patterns, create AI-enabled engineering workflows, and guide engineers through complex technical decisions. They will also lead the Engineering team, establish delivery discipline, and ensure that speed does not come at the expense of security, maintainability, reliability, or product quality. This is an opportunity for a technically exceptional and ambitious leader to take the next step in their career, shape SundaySky's technical future, and demonstrate what an AI-native software organization can accomplish.

Key Responsibilities:

• Own SundaySky's application, platform, data, integration, cloud, and AI architecture.
• Work directly in the codebase through prototyping, implementation, code review, debugging, refactoring, and complex problem-solving.
• Translate product strategy into pragmatic technical designs and executable implementation plans.
• Create reference architectures, reusable components, engineering patterns, and technical standards that accelerate development.
• Make sound tradeoffs between immediate delivery, architectural quality, platform scalability, and technical debt.
• Lead the modernization of legacy components while preserving platform stability and customer commitments.
• Serve as the principal technical escalation point for difficult architecture, performance, security, reliability, integration, and production issues.

AI-Native Software Development

• Design and personally implement an AI-native development model in which AI agents perform a meaningful portion of coding, testing, documentation, analysis, and maintenance.
• Build the context, instructions, tools, guardrails, evaluation methods, and human-review practices required for agents to produce production-quality software.
• Use AI coding tools actively and regularly—not simply as productivity add-ons, but as core components of the development lifecycle.
• Develop internal agents and automated workflows for implementation, testing, code review, debugging, documentation, migration, release readiness, and production support.
• Determine which work can be safely delegated to AI and where experienced engineering judgment must remain directly involved.
• Establish measurable standards for the quality, security, maintainability, and productivity of AI-produced code.
• Continuously evaluate emerging models, tools, frameworks, and agentic development techniques.

Hands-On Technical Leadership

• Lead from within the work by pairing with engineers, reviewing implementations, solving difficult problems, and modeling strong engineering practices.
• Provide clear technical direction to a small, senior, highly leveraged Engineering team.
• Break ambiguous product requirements into well-defined architectures, technical plans, and AI-executable units of work.
• Raise the technical capabilities of the team through coaching, direct collaboration, architectural guidance, and rigorous review.
• Remain close enough to the code and platform to make informed technical decisions without relying entirely on secondhand reporting.
• Create an environment in which engineers have high autonomy, clear ownership, strong standards, and accountability for outcomes.

Delivery and Engineering Operations

• Establish a lightweight, automation-first development lifecycle optimized for speed, quality, and predictability.
• Improve CI/CD, automated testing, observability, security scanning, infrastructure automation, and release management.
• Define clear standards for technical readiness, code review, test coverage, release readiness, and production quality.
• Use metrics such as cycle time, deployment frequency, defect escape rate, reliability, and AI-generated contribution quality to improve execution.
• Partner closely with Product and Design to convert strategy and requirements into executable technical work.
• Provide clear visibility into delivery status, capacity, dependencies, technical risks, and tradeoffs.
• Build and lead a deliberately small team of highly experienced U.S. and nearshore engineers.
• Recruit engineers who combine strong technical judgment with effective use of AI-assisted development.
• Create clear ownership across applications, services, platform components, and production operations.
• Manage performance directly and candidly while minimizing unnecessary organizational layers and process.
• Scale the organization thoughtfully, using automation and AI leverage before defaulting to additional headcount.
• Foster a culture of urgency, experimentation, craftsmanship, accountability, and continuous learning.

Required Qualifications:

• Significant hands-on software engineering experience, including recent and substantive involvement in architecture, implementation, code review, and production problem-solving.
• Deep architectural expertise across modern frontend and backend systems, APIs, data, cloud infrastructure, integrations, security, observability, performance, and reliability.
• Demonstrated use of AI coding agents to build, test, refactor, debug, or maintain production software.
• Ability to design development workflows in which AI performs implementation work under experienced human direction, review, and accountability.
• Experience creating technical specifications, architectural context, coding standards, guardrails, and validation methods that enable reliable agentic development.
• Evidence of personally building prototypes, production capabilities, internal developer tools, agents, or engineering automation.
• Experience providing technical leadership across multiple engineers, teams, or complex initiatives, whether as an architect, distinguished engineer, technical lead, Director, or Engineering executive.
• Ability to lead and develop senior engineers without becoming disconnected from implementation.
• Strong judgment regarding architecture, technical debt, security, scalability, platform reliability, and build-versus-buy decisions.
• Experience modernizing development practices, CI/CD, automated testing, observability, infrastructure, and release processes.
• Ability to operate effectively in a growth-stage environment with ambiguity, legacy constraints, limited resources, and demanding customer commitments.
• Clear, direct communication with technical and nontechnical stakeholders.
• A builder’s mindset, high personal ownership, intellectual curiosity, urgency, and the desire to create something meaningfully different.
• Revised preferred qualifications
• Experience as a principal engineer, distinguished engineer, chief architect, hands-on Engineering Director, startup CTO, or similarly technical leader.
• Experience building AI agents, LLM-enabled applications, agent orchestration systems, evaluation frameworks, or intelligent automation.
• Advanced use of tools such as Claude Code, Codex, Cursor, GitHub Copilot, or comparable agentic development platforms.
• Experience designing systems that combine deterministic software, generative AI, human review, and automated evaluation.
• Experience with cloud-native, API-first, multi-tenant, data-driven enterprise SaaS platforms.
• Experience leading a blended team of U.S.-based and nearshore engineers.
• Experience taking broader organizational ownership after succeeding in a senior technical or architectural role.

This Role Is Not

• A management-only position.
• A role for someone who wants to delegate architecture and implementation entirely to others.
• A traditional executive position centered on board communication, organizational layers, or managing managers.
• An opportunity to scale Engineering primarily by adding headcount.
• A fit for someone whose AI experience is limited to sponsoring adoption or purchasing developer tools.
• A CTO-in-waiting role designed around corporate strategy, fundraising, or external visibility.

Base salary: $260,000-$280,000 annually

Additional compensation and benefits include an annual bonus, equity, 401(k), medical, dental, and vision insurance, short-term disability coverage, and paid parental leave.