Machine Learning Engineer
Role details
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Tech stack
Job description
full-lifecycle production ML pipelines while acting as a technical anchor to adapt global frameworks for (India) localized requirements, ensuring architectural parity and high-performance execution. Drive Applied Decision Science: Design and productionize end-to-end recommendation and classification models integrated directly into merchant-facing products, turning complex data patterns into real-time, actionable insights. Optimize for Performance: Identify and resolve performance bottlenecks in training and inference (memory, latency, throughput) to ensure ML solutions scale seamlessly within a high-throughput production environment. Architect the Intelligence Layer: Own the development of reusable AI components - that serve as the foundation for scaling Generative AI applications. Champion Technical Excellence: Promote and apply software and data engineering best practices while partnering with international MLOps and platform departments to seamlessly adopt internal toolsets., Senior Data Analyst: Neem de leiding over complexe data-initiatieven met SQL, Python en Looker. B... Beste match airflow kubernetes python Data Science Senior MLOps Engineer (ML Workflows Engineering) JetBrains Amsterdam, Netherlands; Belgrade, Serbia; Berlin, Germany; Limassol, Cyprus; Madrid, Spain; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Remote, Germany; Warsaw, Poland; Yerevan, Armenia Goede match docker kubernetes python Datadog Dublin, Ireland; Madrid, Spain; Paris, France Senior Security Engineer - Cloud SIEM: Ontwikkel schaalbare detectie- en responscapaciteiten in D...
Requirements
Who You Are: Experienced Engineer & Python Expert: You have 4+ years of experience in the machine learning domain with expert-level Python skills and deep familiarity with the standard data science toolkit (e.g., PyTorch, TensorFlow, XGBoost/LightGBM, Pandas, and Scikit-learn). Full-Lifecycle Architect: You are proficient in the end-to-end ML lifecycle - from leveraging big data (Spark, SQL/Trino) to build robust pipelines to deploying and maintaining models in production using MLOps best practices. Infrastructure & Automation Savvy: You have hands-on experience with ML infrastructure and orchestration tools such as Kubernetes, Docker, Airflow, Argo-Workflows, and monitoring stacks like Prometheus and Grafana. Experimental & Iterative Mindset: You thrive in a "launch fast and iterate" environment, applying strong foundational knowledge of statistics and ML techniques to solve complex real-world problems. Technical Leader & Communicator: You proactively take ownership of projects from ideation to deployment, with the ability to lead stakeholders and translate complex technical outcomes into clear insights for any audience. Nice to Have: You have experience with distributed GPU compute environments You have experience working with a Machine Learning 'Feature Store' Data positions at Adyen: We know companies handle different definitions for their data-related positions, this is for instance dependent on the size of a company. We categorized and defined all our positions. Have a look at this blogpost to find out! Our Diversity, Equity and Inclusion commitments Our unique approach is a product of o