UPTARGET is looking for a Full-Stack Developer who enjoys building scalable, intelligent platforms from the ground up. This role is ideal for engineers with deep expertise in TypeScript, Cloudflare, Serverless Architectures, and Data Engineering. Advanced English is a must-have for this position. If this sounds like your background and interests, here is a more detailed description of the role:
The project is a fast-growing AI and AdTech platform building semantic intelligence infrastructure that helps enterprises transform complex marketing and performance data into actionable insights through modern AI, cloud, and data technologies.
OUR STACK * TypeScript everywhere. * Cloud: a mix of Cloudflare (likely the home for Workers-style compute, queues, and the agent runtime) and GCP (likely for embeddings via Vertex AI and other data/AI services). * AI agents: leaning toward the Cloudflare Agents SDK, but open if you’ve shipped something better. * Vector DB: open. Could be Cloudflare Vectorize, Vertex AI Vector Search, Turbopuffer, or something else. We’ll pick based on retrieval quality, ops cost, and how cleanly it fits the rest. * Frontend: React. Framework (Remix or TanStack Start) TBD.
YOUR BACKGROUND * Strong TypeScript. Types as a design tool, not a chore. * Production experience with serverless or edge runtimes (Cloudflare Workers, Vercel, Lambda, Deno Deploy, anything in the family). * Have built durable, idempotent ingestion pipelines: queues, retries, backpressure, dedup, schema evolution. * Background in ETL, observability for data pipelines, or eval frameworks for retrieval quality. * Have shipped at least one agent-style system in production with tool use, state, and multi-step workflows. The specific framework matters less than the experience. * React fluency with modern patterns. * Comfortable spanning two clouds. Knows when to lean on edge compute vs. GCP’s data/AI tooling, and how to bridge them (auth, egress, latency).
NICE TO HAVES * Built or operated RAG systems in production. * Familiarity with current embedding models and their dimension, quality, and cost tradeoffs. * Practical understanding of embeddings, chunking, and retrieval quality. * Adtech, performance marketing, or marketing analytics background. Knows what channels, attribution, and creative testing actually look like in production.
