Full Stack AI Engineer (Data)

**Full Stack AI Engineer (Data)** *Build end\-to\-end products on a solid data foundation, with AI as a force multiplier.* Data Practice \| Remote (Global) \| Senior **About TechTorch** At **TechTorch**, we’re building the future of intelligent work. Our mission is to help companies design, build, and deploy **AI agents that automate complex, real\-world workflows** — delivering reliability, measurable ROI, and massive efficiency gains. Here, you won’t just be playing with prompts or running endless proofs of concept. You’ll **ship production\-grade AI systems** that solve real problems across industries. You’ll join a **hands\-on, fast\-moving, ownership\-driven** team that thrives on building quickly, iterating fast, and seeing results in days — not months. **About the Practice** TechTorch's Data Practice sits at the intersection of enterprise data and applied AI. We design and build AI\-native systems that don't just analyze the past — they actively drive decisions. Our work spans data infrastructure and pipelines, intelligent automation, and full\-stack AI applications across industries. We work the way the best client\-delivery teams now operate: small teams, deep ownership, no hand\-offs at boundaries. We take problems from a client whiteboard to production, and we let AI do the heavy lifting wherever it earns its place. **The Role** We're looking for an engineer who builds across the full stack and owns the data underneath it. You can sit in a client session, shape the architecture, design the data foundation, and ship the application that runs on top of it — without handing off at the boundaries. The work spans client delivery and internal accelerator development. You map the problem, structure the solution, and own the outcome from end to end. AI coding agents are central to how we build — not a novelty, but the daily layer that lets a small team cover a lot of ground. **What You'll Do** * Own work end to end — from discovery and solution shaping through system design, build, and production deployment. * Design and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production. * Build full\-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usable. * Use AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or quality. * Design and build AI capabilities where they fit — RAG pipelines, agentic workflows, and LLM\-in\-the\-loop processing — and compose them via MCP servers, Skills, and Plugins. * Orchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the job. * Stand up and own CI/CD and cloud deployments on AWS and Azure. * Translate ambiguous client requirements into clear desig