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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's core technology is the Market Model, a proprietary AI-powered system delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence. Built on a glass-box architecture using market data with full transparency, it has been deployed across global aviation with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul, delivering an average profit uplift of 7%.
The Large Market Modeling (LMM) team is the engine powering Fetcherr's pricing intelligence. This team builds and trains the models that transform market signals, customer behavior, and competitive dynamics into reliable, production-ready demand models.
We are seeking an MLOps Team Lead to drive the development of an internal machine learning platform serving multiple ML teams. This is a hands-on leadership role where you will guide a small team of MLOps engineers building the automation and infrastructure that powers research workflows and production pipelines.
Key Responsibilities:
- Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality
- Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production
- Stay hands-on: contribute to design and code, review work, and set engineering standards
- Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership
- Build and maintain models and data pipelines behind data science workflows, ensuring accuracy, consistency, and efficiency
- Work across structured and unstructured data from multiple sources on a large-scale, distributed platform
- Partner with R&D and other stakeholders to translate research needs into robust, scalable systems
- Help evolve the platform, including ongoing migration from Dask to Ray
This role combines technical depth with people leadership, requiring someone who can set direction while remaining close to the code and infrastructure challenges.
