Full Stack Engineer

European Tech Recruit; A distancia; EUR 40.000 - 60.000; Jornada completa

Descripción de la vacante

Una plataforma de programación en línea busca un Full Stack Engineer para diseñar y desplegar sistemas de IA, optimizar modelos y crear flujos de trabajo escalables. Se requiere experiencia en Python y marcos de ML. Se ofrece trabajo remoto en España o Polonia. Ideal para candidatos con antecedentes en MLOps y herramientas de inteligencia artificial. La empresa promueve un entorno colaboarativo y de innovación.

Formación

• Experiencia en la entrega de componentes de IA de principio a fin.
• Sólidos conocimientos de Python y familiaridad con marcos de ML.
• Experiencia práctica con herramientas de MLOps.

Responsabilidades

• Diseñar sistemas de IA listos para producción.
• Preparar conjuntos de datos de alta calidad y mantener tiendas de características.
• Desarrollar flujos de trabajo y ML escalables.

Conocimientos

Python

Scikit-Learn

TensorFlow

PyTorch

Hugging Face

MLOps

Airflow

Kafka

Spark

Herramientas

MLflow

dbt

BigQuery

Redshift

Snowflake

Descripción del empleo

Full Stack Engineer

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Fully Remote in Spain or Poland

We are working with a leading online scheduling platform designed to simplify the process of coordinating meetings and events. Founded over 18 years ago, it helps individuals and teams avoid the "back-and-forth" of email scheduling by allowing users to propose multiple time slots and let participants vote on their availability.

Responsibilities of the role :

Architect Production AI Systems : Design reliable, production-ready AI systems, selecting optimal tools for robust real-world performance.

Curate Data & Feature Stores : Prepare high-quality datasets and maintain feature stores to ensure data consistency for training and inference.

Build Scalable ML Pipelines : Develop end-to-end data and ML pipelines using Airflow and dbt for seamless ingestion, deployment, and monitoring.

Design & Deploy Models : Prototype and train diverse neural architectures, including LLMs, with a focus on reproducibility and performance.

Implement Advanced Retrieval (RAG) : Design Graph RAG and hybrid retrieval systems, including graph construction and entity linking.

Enable Edge Intelligence : Optimize and quantize large models for efficient on-device and edge processing.

Requirements of the role :

Experience delivering complete AI components—from planning and modeling to deployment, monitoring, and iteration.

Strong Python skills and deep familiarity with ML frameworks such as Scikit-Learn, TensorFlow, PyTorch, and Hugging Face. You’re comfortable designing, evaluating, and prototyping diverse model types.

Hands‑on experience with MLOps tools (e.g., MLflow, ZenML), dbt modeling, and working with cloud data warehouses or data lakes.

Experience building and scheduling pipelines in Airflow. Familiarity with modern data stacks such as Kafka, Spark, and cloud warehouses (BigQuery, Redshift, Snowflake). Ability to define event-level tracking schemas for reliable analytics.

Strong understanding of model behavior and evaluation. Experience developing frameworks for assessing model quality, reliability, hallucination detection, prompt regression, safety scoring, or multi-hop reasoning. Familiarity with RAG, graph-based retrieval, and prompt design. xcskxlj

A focus on shipping systems that are robust, explainable, and usable by others.