AI Engineering Delivery Lead (Beta Launch)

NYC Preferred | Fractional (20–30 hrs/week)

We believe AI is fundamentally changing how software should be designed, built, reviewed, tested, and released. We’re not looking for someone to simply manage engineering delivery. We’re looking for someone to help reinvent it.

Our product is entering Beta, and we’re building an engineering organization where AI is embedded into every stage of the software development lifecycle—from product discovery and engineering planning to code review, QA, release management, and continuous improvement.

We’re looking for a hands-on technical leader who can lead a small team of senior engineers while remaining deeply involved in engineering execution.

This is not a software architecture role, nor is it a traditional project management position.

Instead, you’ll become the operational leader responsible for helping us build an AI-first engineering organization capable of shipping faster, with higher quality, and greater confidence than traditional software teams.

If you’re constantly experimenting with new AI tools, challenging inefficient processes, and inventing better ways to build software, we’d love to meet you.

Your Mission

Your mission is simple:

Help us become one of the most AI-native software engineering organizations in healthcare.

You’ll continuously improve how ideas become software by combining technical leadership, AI, automation, and disciplined engineering practices.

Every workflow should become faster. Every release should become safer. Every engineer should become more productive.

What You’ll Own

You will own the engineering delivery process from idea to production.

This includes:

• Engineering delivery and release management
• GitHub workflows and engineering standards
• AI-assisted engineering workflows
• QA strategy and release readiness
• Delivery operations
• Vendor accountability
• Engineering communication
• Beta execution
• Continuous process innovation

You’ll work directly with company leadership, engineering, QA, DevOps, design, vendors, and Beta users to ensure every release is production-ready.

Innovation Expectations

Innovation is not optional. It is one of your primary responsibilities.

We expect you to constantly ask:

• What can I do with AI that otherwise would be impossible?
• What can AI do instead of us?
• What repetitive work should disappear?
• How can engineers spend more time building?
• How can QA become dramatically more efficient?
• How can GitHub become smarter?
• How can releases become safer?
• How can engineering move twice as fast without sacrificing quality?

Every quarter, you’ll be expected to introduce measurable improvements that increase engineering productivity, software quality, or delivery speed.

Responsibilities Engineering Leadership

• Lead the daily operating rhythm across Engineering, QA, DevOps, Product, Design, Vendors, and Leadership.
• Coordinate a small team of senior engineers.
• Translate founder discussions, customer feedback, Slack conversations, bugs, and feature requests into engineering-ready work.
• Define clear acceptance criteria.
• Prioritize engineering work.
• Remove blockers.
• Manage dependencies.
• Drive release readiness.
• Hold engineering vendors accountable for quality, communication, timelines, and execution.

GitHub & Release Management

• Lead the migration from GitLab to GitHub.
• Design GitHub workflows.
• Improve Pull Request processes.
• Implement Branch Protection rules.
• Improve Release Branch strategy.
• Build Release Gates.
• Improve GitHub Actions workflows.
• Improve CI/CD reliability.
• Strengthen deployment confidence.
• Improve release documentation and visibility.

AI-First Engineering

You will actively use AI every day.

Examples include:

• AI-assisted code reviews
• AI-assisted pull request analysis
• AI-generated QA strategies
• AI-generated regression testing
• AI-generated engineering tickets
• AI-generated acceptance criteria
• AI-generated release notes
• AI-assisted debugging
• AI-assisted root cause analysis
• AI-assisted documentation
• AI-assisted backlog refinement
• AI-assisted engineering communication

You’ll continuously prototype new AI workflows and evaluate emerging tools that improve engineering productivity.

Engineering Excellence

Continuously improve:

• Developer productivity
• Release quality
• Engineering standards
• QA effectiveness
• Delivery predictability
• Technical documentation
• Engineering visibility
• Operational efficiency

Required Experience

We’re looking for builders—not coordinators.

You should have:

• 7+ years leading technical software delivery.
• Experience taking software products through Beta and production launch.
• Experience leading senior engineering teams.
• Strong understanding of modern software engineering practices.
• Excellent understanding of GitHub workflows, including:
• Pull Requests
• Branch Protection
• GitHub Actions
• Release Branches
• Merge Strategies
• Code Review
• Experience coordinating Engineering, QA, DevOps, Product, and Design.
• Experience managing offshore engineering teams or software vendors.
• Working knowledge of AWS, containers, deployment pipelines, CI/CD, and cloud infrastructure.
• Excellent written and verbal communication skills.
• Comfortable operating in fast-moving startup environments where priorities evolve daily.
• Comfortable challenging assumptions—including those of founders—when a better technical solution exists.

AI Requirements

AI is central to this role.

We’re looking for someone who already uses AI as part of their daily engineering workflow.

Hands-on experience with several of the following is strongly preferred:

• Claude Code
• Claude Projects / Teams
• Cursor
• GitHub Copilot
• ChatGPT
• Amazon Bedrock
• Gemini
• Windsurf
• MCP Servers
• AI Agents
• Prompt Engineering
• Engineering Automation

You should be able to demonstrate real-world experience using AI for:

• Code review
• Pull request analysis
• QA planning
• Test generation
• Bug investigation
• Root cause analysis
• Documentation
• Technical writing
• Engineering communication
• Release planning
• Workflow automation

Nice to Have

• Startup experience
• Marketplace products
• Mobile applications
• Healthcare or regulated software
• Docker
• Kubernetes
• EKS
• ECR
• ArgoCD
• Terraform
• CloudFormation
• AI agent orchestration
• Developer productivity tooling
• Internal engineering platforms

What Success Looks Like

Within your first 90 days, you will have:

• Established a predictable engineering operating rhythm.
• Successfully migrated engineering delivery to GitHub.
• Improved release quality and deployment confidence.
• Reduced engineering cycle time.
• Introduced AI-assisted code review workflows.
• Implemented AI-assisted QA processes.
• Strengthened GitHub standards and release discipline.
• Increased engineering productivity by automating repetitive work.
• Built repeatable AI-powered engineering workflows.
• Improved vendor accountability.
• Reduced release risk.
• Helped prepare the platform for a successful Beta launch.

Within your first year, you will have:

• Built an AI-first engineering delivery organization.
• Introduced multiple engineering innovations adopted across the team.
• Significantly improved engineering velocity without compromising quality.
• Established a culture of continuous experimentation and technical excellence.

What This Role Is Not

This is not a traditional Project Manager or Scrum Master role.

You’re not here to:

• Run meetings all day.
• Create process for the sake of process.
• Maintain Jira boards.
• Track status updates.
• Slow engineers down with unnecessary ceremony.

You’re here to help exceptional engineers build exceptional software.

Interview Process

We believe résumés don’t predict performance.

Candidates should expect a collaborative working session where we’ll solve real engineering problems together.

You may be asked to:

• Review a GitHub Pull Request.
• Design an AI-assisted QA strategy.
• Build an AI-powered engineering workflow.
• Turn a founder discussion into engineering-ready tickets.
• Improve an existing delivery process.
• Design a GitHub workflow.
• Explain how you would use Claude Code to improve engineering quality.
• Solve a real delivery challenge using AI.

We’re far more interested in how you think than in memorized interview answers.

Engagement

• Fractional engagement (20–30 hours/week) to start.
• Strong opportunity to grow into a long-term leadership role.
• Direct collaboration with company leadership.
• High ownership.
• Significant influence over how our engineering organization evolves.

Location

NYC-based candidates are strongly preferred.

Candidates within commuting distance of New York City who can meet in person when needed will receive priority consideration.

Who This Is For

This role is for someone who sees AI as more than a productivity tool.

You believe AI is transforming software engineering, and you’re excited to help define what that future looks like.

You’re equally comfortable discussing GitHub workflows, reviewing code, designing AI-powered QA processes, coaching engineers, improving delivery systems, and challenging conventional thinking.

You don’t wait for better tools—you build better ways of working.

If you’re passionate about combining engineering excellence, AI, and continuous innovation to create world-class software delivery, we’d love to hear from you.

Pay: $2,400.00 - $2,800.00 per week

Application Question(s):

• This role includes a live, hands-on technical working session. Candidates will be asked to solve a real engineering and software delivery challenge using Claude Code and any AI engineering tools they normally use. The exercise may include code review, debugging, QA strategy, GitHub workflows, release planning, engineering ticket creation, or workflow automation. This is not a coding quiz—we want to understand how you think, leverage AI, and solve real engineering problems.

Are you comfortable participating? If yes, briefly describe your experience using Claude Code or similar AI tools and provide one real example of how AI improved your engineering workflow.

Work Location: Hybrid remote in New York, NY 10023