Summary
✨ AI‑Generated
An MLOps engineering role supporting a team that builds and trains production-grade demand models from complex market signals. You will help turn advanced machine learning models into reliable, scalable systems that power real-time commercial decision-making across dynamic markets.
Highlights
Opportunity to work on production-grade AI and machine learning systems with measurable commercial impact. The role sits close to the core modeling team and offers experience with demand forecasting, market signals, model training, and reliable deployment of advanced AI systems.
Description
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.
The Large Market Modeling (LMM) team is the engine underneath Fetcherr's pricing intelligence.
We're the ones who actually build and train the models — taking a chaotic world of market signals, customer behavior, and competitive dynamics and turning them into reliable, production-ready demand models.
Other teams at Fetcherr work with the models; we're the ones who bring them to life.
Think of us as the team that teaches Fetcherr's AI how people buy — so it can always recommend the right price at the right moment.
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.
