W skrócie
- Zadania: Prowadź zespół MLOps, budując platformę ML i automatyzując procesy.
- Firma: Fetcherr - innowacyjna firma AI z przejrzystymi modelami rynkowymi.
- Benefity: Atrakcyjne wynagrodzenie, rozwój zawodowy i praca w dynamicznym środowisku.
- Pozostałe informacje: Możliwość pracy nad projektami w różnych branżach.
- Dlaczego ta praca: Twórz wpływowe systemy AI w szybko rozwijającym się startupie.
- Kwalifikacje: Doświadczenie w Pythonie, MLOps i zarządzaniu zespołem.
Przewidywane wynagrodzenie mieści się w przedziale 135000 - 165000 zł za rok.
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 looking for a MLOps Team Lead to drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of 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 raise the bar by example.
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.
- Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.
- Help evolve the platform, including our ongoing migration from Dask to Ray.
Requirements:
- BSc or Master's degree in Computer Science, Mathematics, or Engineering.
- At least 5 years of commercial experience in Python.
- At least 3 years of hands-on commercial MLOps experience in production (not side projects).
- Experience managing or leading a team of engineers, with ownership of both people and delivery.
- Hands-on experience owning the ML model lifecycle (training, deployment, monitoring, retraining).
- Experience with pipeline orchestrators such as Dagster or Airflow.
- Experience with a major cloud provider such as GCP, AWS, or Azure.
- Experience with distributed computing systems.
- Experience with Docker.
- Experience with Kubernetes.
- Commercial experience writing and maintaining scalable ML systems.
- Fluent in English, both written and spoken.
Nice to have:
- Experience with Dagster (Advantage).
- Experience with Dask or Ray (Advantage).
- Experience with Spark, including implementing distributed algorithms in Python (Advantage).
- Experience with traditional predictive, forecasting, or pricing-optimization ML systems (Advantage).
- Experience in aviation, demand forecasting, or price optimization (Advantage).
- Good understanding of data structures and algorithms (Advantage).
If you are excited about building impactful AI systems in a high-growth startup environment, and want to help redefine how industries price, forecast, and optimize, we would love to hear from you.
MLOps Team Lead in Kraków – pracodawca: Fetcherr
Fetcherr to doskonały pracodawca, który stawia na rozwój swoich pracowników w dynamicznym środowisku startupowym. Oferujemy możliwość pracy z najnowocześniejszymi technologiami w dziedzinie sztucznej inteligencji oraz współpracę z zespołami międzyfunkcyjnymi, co sprzyja innowacyjności i kreatywności. Nasza kultura pracy opiera się na przejrzystości, współpracy i dążeniu do osiągania wymiernych rezultatów, co czyni nas idealnym miejscem dla osób pasjonujących się uczeniem maszynowym i jego zastosowaniami w realnym świecie.
