Contract Product & Data Systems Engineer — AI Platform

Project: Short-term contract Estimated duration: 6–10 weeks, with potential for occasional ongoing support Location: Remote We are building an early-stage private search and recommendation platform centered on high-quality professional and service-provider intelligence. We are looking for a hands-on Product & Data Systems Engineer to take ownership of the initial technical and data infrastructure. The goal is not to over-engineer the product. We need someone who can take an existing early-stage stack, organize it properly, implement the right workflows, and leave behind a clean, scalable and well-documented foundation. The current environment includes Emergent, Supabase/PostgreSQL, Airtable and AI/LLM-based research and data workflows. This is an execution role. Much of the product direction, UX and business logic has already been defined. We need someone who can translate that into reliable systems so the founder does not need to personally manage the technical implementation. What you'll work on Review the existing product and recommend a pragmatic technical architecture Configure and manage Supabase/PostgreSQL, including database structure, authentication and permissions Design the underlying relational data model for professionals/providers, recommendations, sources, categories, locations and supporting research Structure and optimize Airtable as an early-stage data-management and operational system Establish workflows between Airtable, Supabase and the product Work with and maintain an application being developed using Emergent / AI-assisted development Review AI-generated code and architecture rather than treating AI-generated output as production-ready by default Build or configure APIs, integrations, automations and data pipelines where appropriate Develop AI-assisted workflows for research, data extraction, classification and enrichment Incorporate human review/approval into AI research workflows Establish data-quality processes including deduplication, normalization, verification status and source/provenance tracking Ensure confidential source information can be stored internally without being exposed in the customer-facing product Establish appropriate security, permissions, environment variables, backups and access controls Set up GitHub/version control and basic development/deployment processes Document the architecture, database schema, workflows and operating procedures so the system can be maintained after the engagement What we're looking for Strong experience with: Supabase and PostgreSQL Relational database design and data modeling Airtable, including linked records and automations APIs, JSON, webhooks and system integrations AI/LLM APIs and structured-output workflows AI-assisted development tools Data quality, normalization and deduplication Authentication, permissions and data security Git/GitHub Modern web application architecture Experience with React/Next.js or similar front-end frameworks is helpful, but this is not primarily a front-end design role. We are particularly interested in someone who has worked in an early-stage startup environment and understands when to build something properly versus when a lightweight solution is sufficient. The ideal person You are comfortable inheriting something that is still evolving. You can look at an Airtable base, a Supabase project, an AI-built application and a set of business requirements and determine how the pieces should fit together. You think carefully about data architecture and provenance, not simply application screens. You are equally comfortable saying “Airtable is sufficient for this right now” and “This needs to move into Postgres before we scale it.” You use AI development tools extensively but understand enough engineering to recognize when their output needs to be corrected. Initial engagement deliverables By the end of the project, we would like to have: A documented technical architecture Clean, structured Airtable environment Properly designed Supabase/PostgreSQL schema Defined data model and source/provenance structure Working data flows and integrations AI-assisted research/enrichment workflow with human review Appropriate permissions and security controls GitHub/version-control and deployment process Documentation allowing the founder or another developer to maintain the system Recommendations for the next stage of development When applying Please send: Examples of early-stage products or internal systems you've built Specific experience with Supabase/Postgres and Airtable Examples of AI/LLM workflows you've implemented Your experience working with AI-assisted coding/building tools An example of a database or data architecture you designed Your availability over the next 6–10 weeks Hourly rate or proposed project structure Please do not send a generic full-stack developer application. We're specifically looking for someone who combines product engineering, data architecture and AI workflow experience.