MLOps Engineer

Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets. Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul. We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition. You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system. Requirements: BSc or Master's degree in Computer Science / Math / EngineeringAt least 5 years of commercial experience in PythonAt least 3 years hands-on MLOps commercial experienceExperience working with pipeline orchestrators (e.g., Dagster, Airflow)Experience with distributed computing systemsExperience with Docker and Kubernetes or other scalable containerized solutionsCommercial experience in writing and maintaining scalable ML systemsFluent in English, both written and spokenTeam player, ready to help others Nice to have: Good understanding of Data Structures and AlgorithmsPro-active with tasks, often suggesting different/better ideas If you're excited about building impactful AI systems in a high-growth startup environment, and want to help redefine how industries price, forecast, and optimize, we’d love to hear from you.