Forward Deployed AI Engineer

Techtorch · EU + UKsenior

En corto

▸Ingeniero de IA desplegado en cliente, construye sistemas completos de datos y AI desde cero.
▸Día a día: diseño de datos, pipelines, aplicaciones full-stack y uso de agentes de IA para acelerar el desarrollo.
▸Lo destacado: los agentes de IA (como Claude Code) son parte central del flujo de trabajo diario, no solo una prueba de concepto.

Fluent English required for client-facing work and collaboration.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Experiencia sólida en ingeniería de datos con modelado dimensional y SCD.
✓Construcción y mantenimiento de pipelines ETL/ELT en producción.
✓Uso avanzado de dbt con transformaciones modulares, pruebas y documentación.
✓Desarrollo full-stack con Python/FastAPI y Next.js.
✓Experiencia con CI/CD y despliegue en AWS o Azure.
✓Capacidad para traducir requisitos ambiguos en arquitecturas claras y comunicar trade-offs.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonFastAPINext.jsdbtSnowflakeDatabricksAWSAzureAirflowDagster

¿A quién escribirle en Techtorch?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Forward Deployed AI Engineer Build end-to-end products on a solid data foundation, with AI as a force multiplier. Data Practice | Remote (Poland) | 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. 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. Must Have We're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with either. Data Engineering Foundation Data modeling and schema design — dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defend. Hands-on data pipeline experience — ETL/ELT design across batch and incremental loads, built and maintained in production (not just SQL scripts on a schedule). Slowly Changing Dimensions (SCD) and change-data handling — knows the patterns and when each applies. dbt Experience— modular SQL transformations, tests, documentation, and incremental strategies. , Snowflake, Databricks, or a comparable cloud warehouse/lakehouse). Data quality thinking — testing, validation, and lineage treated as first-class, not afterthoughts.

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