Full Stack Engineer

hace 2 semanas

Valencia, España European Tech Recruit A tiempo completo

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

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