AI Engineer

This is a fully remote position, open to applicants in Spain.

📋 Description

• Create effective AI, machine learning, and optimization solutions for both technical and operational teams.

• Utilize large language models, foundational models, and multimodal techniques for search, analysis, knowledge discovery, and decision-making support.

• Develop AI-driven applications that integrate models, data pipelines, prompts, agents, evaluation mechanisms, optimization logic, and user input.

• Design and implement knowledge graphs, ontologies, and structured domain knowledge.

• Convert scientific, engineering, process control, and operational inquiries into data products, model workflows, optimization strategies, and user-friendly analytics.

• Create pipelines for both structured and unstructured data, encompassing time-series, laboratory, manufacturing, document, and scientific or technical knowledge sources.

• Participate in model validation, monitoring, documentation, governance, and continuous enhancement.

• Work collaboratively with teams across data science, engineering, digital technology, operations, and business sectors.

• Be prepared to travel regularly, potentially up to around 35% annually, including occasional on-site periods of 2–3 consecutive weeks.

⛳️ Requirements

• Master’s or PhD degree in Chemical Engineering, Biochemical Engineering, Bioinformatics, Process Control, Computer Science, Data Science, or Applied Mathematics.

• Proven industrial experience with a successful track record of achieving measurable business impact in production or operational settings.

• Practical experience with Python and contemporary machine learning workflows, covering data preparation, modeling, validation, deployment, and monitoring.

• Expertise in developing end-to-end AI or machine learning applications and implementing solutions in real-world industrial or production contexts.

• Knowledge of frontier model capabilities, prompt design, agentic AI, retrieval-augmented generation, evaluation, hallucination reduction, and human-in-the-loop workflows.

• Experience with both structured and unstructured data, including time-series, scientific, engineering, document, knowledge base, or operational datasets.

• Proficiency in mathematical optimization, process control, forecasting, anomaly detection, recommendation systems, natural language interfaces, classification, deep learning, or predictive analytics.

• Familiarity with version control systems, testing, APIs, containers, CI/CD practices, and maintainable code design.

• Strong communication skills to convey model outputs, uncertainties, assumptions, control logic, optimization trade-offs, and practical implications.

• Experience with knowledge graphs, ontologies, semantic modeling, graph databases, or RAG for scientific, industrial, or operational applications.

• Background in manufacturing, process development, industrial operations, supply chain, biomanufacturing, bioinformatics, chemical processes, advanced process control, or mathematical optimization.

• Experience with model registries, experiment tracking, observability, prompt and version management, evaluation frameworks, cloud platforms, or production ML systems.

🏝️ Benefits

• Remote working arrangement.

• Opportunities for learning and professional development.

• Collaborative work environment.

• Regular travel for impactful engagement.

• Exposure to a variety of technical communities.

• An inclusive workplace.