Forward Deployed AI Engineer
Role Summary
Join TechTorch as a Forward Deployed AI Engineer to build AI-driven solutions and applications, taking ownership from discovery to production.
About the Organisation
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.
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.
Key Responsibilities
- 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 designs and communicate trade-offs to both technical and business audiences.
- Contribute reusable accelerators and technical assets back to the Data Practice.
Minimum Requirements
We're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with either.
Not required to apply — but these are the things that make a candidate stand out.
Working Conditions
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- Fully remote — work from anywhere, globally.
- Semi-annual team offsites — we come together in person at least twice a year to connect, recharge, and do the work that's better face-to-face.
- High-autonomy, high-ownership work across the full arc of real client problems — not toy datasets or boxed-in tickets.
- A team that takes AI tooling seriously and expects you to use it, not just name-drop it.
- Access to the full modern data and AI stack — no one-tool shops.
- Room to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.
Eligibility Criteria
The eligibility for this role is unclear, as the job description does not specify whether international applicants are welcome.
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