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Senior Geospatial Backend / Platform Engineer (US - Remote)

Brain Trust Inc

United States

United States

About

Our Mission Remember to check your CV before applying Also, ensure you read through all the requirements related to this role. The planet is undergoing one of the most consequential infrastructure transformations in a century. AI compute demand, electrification, decarbonization, and digital acceleration are putting major pressure on the global grid. TRL Real Estate’s Power Properties Division is building the intelligence layer that maps, scores, and activates latent energy land assets — turning brownfields, former power stations, and grid-adjacent parcels into the next generation of hyperscale compute and clean energy campuses. PowerParcels AI™ is an orbital energy grid intelligence platform. It is designed to ingest satellite imagery, utility interconnect queues, LMP pricing feeds, PJM/ERCOT/ISO/RTO market signals, and parcel ownership data to surface high-value acquisition targets that would take a human analyst months to identify. We are now building the backend foundation that powers this platform — and we need an exceptional engineer to help us build the MVP. Engagement Details US-based remote contractor preferred. Full-time contract for initial MVP build Initial MVP phase expected to be approximately 4–6 weeks, with strong potential for extension. Long-term leadership opportunity possible for the right person as the platform and team scale. What You’ll Build You will help build the backend foundation for a platform designed to scale across satellite imagery, grid intelligence, energy market data, parcel analytics, and partner integrations. The initial MVP mandate is focused on standing up the core backend architecture, geospatial data foundation, secure API layer, and initial AI-agent workflows. 1. Geospatial Data Infrastructure Deploy and configure a secure Supabase/PostgreSQL database with PostGIS and pgvector extensions. Build the spatial data model to ingest, index, and serve parcel boundaries, grid proximity corridors, fiber routes, and utility infrastructure layers. Ensure geometry data flows cleanly from ingestion APIs into the frontend Leaflet.js map experience. 2. Secure Backend API Design and build a FastAPI backend that connects geospatial intelligence, parcel data, market pricing layers, and partner data feeds. Migrate exposed frontend API key configurations into a secure server-side environment. Architect RESTful endpoints to support the platform’s core intelligence modules. 3. AI-Agent Workflows Develop the initial LLM-powered agent layer using LangChain, CrewAI, Claude APIs, OpenAI APIs, LlamaIndex, or equivalent tooling. Build MVP workflows that synthesize parcel data, grid proximity scores, LMP pricing, utility interconnect documentation, and AI-generated landowner outreach drafts. Connect vector search, including pgvector or similar tooling, to the agent layer to support RAG-augmented site analysis. 4. Platform Integrations Support initial integrations with LMP / wholesale power pricing feeds, utility/grid data, satellite imagery, and external parcel/title/zoning/infrastructure data sources. Help determine which integrations should be prioritized for the MVP versus future platform phases. Build integration patterns that can scale as new partner APIs and data sources are added. 5. Architecture for Scale Define the technical roadmap for scaling the MVP into an enterprise-grade, investor-ready SaaS platform. Document architecture decisions clearly for future team members and due diligence reviewers. Help identify when additional engineering support may be needed as the platform expands. Responsibilities: Design and build the backend architecture for the PowerParcel AI MVP. Set up a secure Supabase/PostgreSQL database with PostGIS and pgvector. Build a FastAPI backend to connect the frontend map experience with geospatial, parcel, market, and partner data layers. Create initial AI-agent workflows using tools such as LangChain, CrewAI, Claude APIs, or similar LLM tooling. Support workflows that connect parcel/grid proximity, utility documentation, market pricing/LMP data, and AI-generated landowner outreach drafts. Move exposed frontend API key configurations into a secure backend environment. Integrate backend APIs cleanly with the existing Leaflet.js / JavaScript frontend prototype. Help define the technical architecture needed to scale beyond the MVP. Required Experience: 5+ years of professional Python development in production environments. Strong command of FastAPI, Flask, or equivalent Python backend frameworks. Strong hands-on experience with PostgreSQL and PostGIS for spatial data modeling. Demonstrated track record building geospatial, mapping, location intelligence, parcel, infrastructure, or logistics data products. Experience designing backend APIs and data models for early-stage or MVP-phase products under time pressure. Hands-on work with LLM APIs, AI-agent frameworks, vector databases, tool-calling, RAG pipelines, or structured AI application workflows. Ability to integrate backend services cleanly with an existing JavaScript/Leaflet.js frontend. Comfort making strong architecture decisions in ambiguous, fast-moving environments. Strong Differentiators Candidates with the following backgrounds will advance to the top of our review queue: Experience with pgvector, Supabase, or managed Postgres/vector environments. Fluency with LangChain, CrewAI, Claude APIs, OpenAI APIs, LlamaIndex, or similar LLM orchestration frameworks. Background in satellite imagery analysis, geospatial intelligence, remote sensing, or grid infrastructure data. Domain knowledge in energy markets: PJM/ERCOT/ISO-RTO structures, LMP pricing, interconnect queues, bilateral PPAs, or BTM power generation. Experience in PropTech, data center site selection, land intelligence platforms, real estate development, or parcel analytics. Founding engineer, technical lead, software architect, or investor-facing startup experience. xywuqvp
Remote working/work at home options are available for this role.

United States

Languages

English

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