SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's proprietary Market Model delivers accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture using market data with full transparency, the technology is deployed across volatile markets including global aviation with corporate partners like Delta, Virgin Atlantic, WestJet, and Azul, delivering consistent average profit uplift of 7%.
The Large Market Modeling (LMM) team is the engine underneath Fetcherr's pricing intelligence, building and training 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 powering research workflows and production pipelines. You will own end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones while staying close enough to the code to set technical direction and lead by example.
Key responsibilities include leading, mentoring, and growing a team of MLOps engineers while owning delivery and technical quality. You will take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production. The role requires staying hands-on by contributing to design and code, reviewing work, and setting engineering standards. You will drive the team through critical milestones in ML model-lifecycle and infrastructure ownership, partner with R&D and other stakeholders to translate research needs into robust, scalable systems, and help evolve the platform including ongoing migration from Dask to Ray. You will be responsible for building and maintaining models and data pipelines behind data science workflows, ensuring accuracy, consistency, and efficiency of data used for training and inference across structured and unstructured data from many sources on a large-scale, distributed platform.
