Join Hire Hangar and work with fast-growing general companies while building a long-term career.Job Title Full-Stack AI EngineerLocation RemoteTime Zone US Time Zones (EST–PST)Role OverviewWe are seeking a senior Full-Stack AI Engineer to join our platform team and own the end-to-end build of a data ingestion and intelligence layer for enterprise customers in the media vertical.
This is a high-impact, high-ownership role at the intersection of AI, data engineering, and product.
You will architect and ship pipelines that transform unstructured creative assets and ad performance data into a semantic, vector-based layer — and expose it through AI agents and a polished React frontend.
We move fast, ship to production, and hold a high bar for craft and reliability.What You'll BuildA dual-mode data ingestion engine handling creative components (CTAs, headers, body copy, images, video, and metadata) and ad performance data (channel-level signals tied to those components)Multi-modal embedding generation and storage — vector plus structured — optimised for retrieval quality and costAI agent tooling that enables natural language search, comparison, and reasoning across the creative and performance data layerA React frontend that lets users explore the creative library, query performance data in plain English, and surface actionable optimisation insightsAudit-logged decisioning and governance infrastructure to meet enterprise-grade requirementsKey ResponsibilitiesDesign and build durable, idempotent ingestion pipelines for creative and performance data at scale (queues, retries, backpressure, dedup,



schema evolution)Generate and manage embeddings for multi-modal creative assets; select and operate the right vector store for the workloadBuild and maintain retrieval pipelines that serve AI agent tools with accurate, low-latency responsesShip agent-style systems with tool use, state management, and multi-step reasoning workflowsDevelop and maintain the React frontend for the creative intelligence library and query interfaceOwn the full lifecycle of your systems: design, build, deploy, monitor, and iterateContribute to stack decisions with clear reasoning grounded in production experienceCollaborate closely with product and enterprise partners to translate requirements into reliable, scalable systemsRequired QualificationsStrong TypeScript — you use types as a design tool, not a formalityProduction experience with serverless or edge runtimes (Cloudflare Workers, Vercel, Lambda, Deno Deploy, or equivalent)Demonstrated experience building durable, idempotent ingestion pipelines with queuing, retry logic, backpressure handling, deduplication, and schema evolutionPractical, production-level understanding of embeddings, chunking strategies, and retrieval quality tuningAt least one agent-style system shipped to production: tool use,



stateful multi-step workflows — framework matters less than the experienceReact fluency with modern patterns and component architectureComfort operating across two cloud environments; able to reason clearly about when to use edge compute vs. managed data/AI services, and how to bridge themMust have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Linear, or similar), and have ideally worked with US or UK-based companies.
Applications without this experience will not be considered.Preferred QualificationsExperience building or operating RAG systems in productionFamiliarity with current embedding models and the tradeoffs across dimension, quality, and costBackground in ETL design, observability for data pipelines, or evaluation frameworks for retrieval qualityAdtech, performance marketing, or marketing analytics background — understanding what channels, attribution, and creative testing look like in a live production contextOpinions on vector databases (Cloudflare Vectorize, Vertex AI Vector Search, Turbopuffer, or similar) backed by hands‐on experienceTools & TechnologyTypeScript (primary language across the stack)Cloudflare Workers, Queues, and Agents SDK (or equivalent edge runtime)GCP — Vertex AI for embeddings and related data/AI servicesVector database (to be selected: Cloudflare Vectorize, Vertex AI Vector Search, Turbopuffer, or similar)React with Remix or TanStack Start (TBD)Google Workspace, Slack, Zoom, and standard remote collaboration tooling
#J-*****-Ljbffr

📌 Full-Stack Ai Engineer (Rosario)
🏢 Hire Hangar
📍 Rosario