I’m a successful tech entrepreneur starting a small, focused venture studio to iteratively build several private and early-stage software products.
I work from clear, detailed “product constitutions” / executable specs and use AI coding tools (Claude Code, Cursor, etc.) heavily. I’m not looking for someone to just write code. I’m looking for a senior, product-minded engineer who can help me architect systems correctly from the start, avoid early mistakes, and guide AI-assisted development.
This is a fractional, high-judgment role focused on architecture, sequencing, and leverage — not volume coding.
What you’ll do:
- Translate product constitutions into clean, scalable system architecture
- Design data models, access rules, and invariants
- Decide what belongs in v1 vs what should wait
- Set guardrails for AI-assisted development (what AI can and can’t touch)
- Review and improve AI-generated code
- Occasionally implement critical pieces where judgment matters
- Help establish reusable patterns across multiple products
You will NOT be:
- Managing a team
- Working from vague requirements
- Over-engineering infrastructure
- Building pixel-perfect UI from design files
This role is ideal for a senior IC or ex-founder who enjoys early-stage product architecture, values clarity over process, and is excited about AI-augmented development.
Responsibilities:
- Architecture & System Design
- Translate product constitutions, executable specs, and business requirements into clean, scalable system
- architectures
- Design data models, access rules, invariants, and API contracts that hold up as products grow
- Make critical build-vs-wait decisions — what belongs in v1, what should be deferred, and what should
- never be built at all
- Establish the integration architecture connecting corporate systems (Paylocity, Jira, financial systems, Google Workspace) into a coherent data layer
- Design and implement the connectivity layer that allows external software — including third-party SaaS tools, partner systems, and internal services — to plug into the MOS and other company platforms via APIs, MCP servers, webhooks, and other interoperability protocols
- Design modular, composable system boundaries so new capabilities can be added without rewriting existing ones
- AI-Assisted Development Leadership
- Set guardrails and standards for AI-assisted development across engineering efforts
- Define what AI coding tools can and can't touch — where automated generation is safe and where human judgment is required
- Review, refactor, and improve AI-generated code to meet production quality standards
- Establish reusable patterns, templates, and architectural conventions that AI tools can follow consistently
- Evaluate and integrate emerging AI development tools into company workflows
- Product Engineering
- Personally implement critical system components where architectural judgment matters — integration layers, data pipelines, security boundaries, and core business logic
- Collaborate with the Senior AI Engineer on the MOS platform's agentic architecture, agent communication protocols (A2A, MCP), and module design
- Build and maintain MCP servers and API integrations that allow the MOS to read from, write to, and orchestrate actions across the company's external tool ecosystem (e.g., Asana, Jira, Slack, Salesforce, Google, Workspace, Paylocity)
- Build and validate proof-of-concept implementations for new product capabilities before committing to full builds
- Ensure that the natural-language configuration layer, proactive communication engine, and agent discovery framework are architecturally sound
- Cross-Product Leverage
- Establish reusable architectural patterns, shared infrastructure, and common libraries across product portfolios.
- Create and maintain technical decision records so architectural choices are documented and revisitable
- Ensure consistency in how products handle authentication, authorization, data access, inter-service communication, and external system integrations
- Define standard patterns for how new external tools and data sources are connected to company platforms ensuring every integration follows a consistent, secure, and maintainable approach
- Identify opportunities to extract shared capabilities into platform-level services
- Required Qualifications
- 10+ years of software engineering experience, with a strong track record of early-stage product architecture and system design
- Deep experience making v1 scoping decisions — knowing what to build now, what to defer, and what to cut
- Strong data modeling skills — you can design schemas, invariants, and access rules that hold up under real-world complexity
- Hands-on proficiency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or equivalent) and a clear philosophy on how to use them effectively
- Full-stack capability with a backend emphasis — comfortable across API design, data pipelines, infrastructure, and frontend integration
- Excellent architectural judgment — you design systems that are simple enough to ship and flexible enough to evolve
- Strong API design and systems integration skills — deep experience with REST, GraphQL, webhooks, real-time data sync, and building connectors between disparate software systems
- Familiarity with LLM application patterns including RAG, prompt engineering, agentic workflows, and tool-use architectures
- Working knowledge of MCP (Model Context Protocol) or similar protocol-based interoperability standards for connecting AI agents to external tools and data sources
- Ability to read, review, and substantially improve code generated by AI — not just accept it
Preferred Qualifications
- Founder or early-stage engineering experience — you've built products from zero to one and made hard tradeoff decisions with limited resources
- Experience with multi-agent system design, orchestration patterns, and protocol-based interoperability (A2A, MCP, or equivalent)
- Hands-on experience building MCP servers or similar integration layers that expose external SaaS tools to AI-native platforms
- Familiarity with enterprise SaaS integration — particularly Paylocity, Jira, Asana, Salesforce, Culture Amp, Slack, or Guru APIs
- Experience replacing or consolidating enterprise SaaS tools with custom-built platforms
- Background in management operating systems, OKR frameworks, or corporate strategy tooling
- Understanding of compliance and data privacy frameworks (SOC 2, etc.)
- Experience establishing engineering standards and development conventions for small, high-output teams
- Track record of mentoring or guiding other engineers through architectural decisions without formal management authority
- Application Question(s):
- How do you currently use AI coding tools (Claude, Cursor, Copilot, etc.) in your development workflow?
- What is your hourly rate and how many hours per week are you available for a fractional engagement?
- Briefly describe a product you helped architect early that later evolved significantly. What decisions did you make early that mattered most?
Work Location: Must be located in Puerto Rico-Hybrid remote in San Juan, PR 00901
Work Location: Hybrid remote in San Juan, PR 00901
