Train and deploy ML models for retrieval, reranking, and search relevance, such as embedding models and cross-encoders, in production - Deve
We are looking for a Senior AI Engineer to own and scale the AI layer of one of our core D2C subscription products.
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Als AI Solutions Architect gestaltest du die strategische und technologische Weiterentwicklung Künstlicher Intelligenz in den IT-Services de
Als AI Engineer entwickelst und implementierst du innovative KI-Lösungen auf Basis moderner Large Language Models (LLMs) und begleitest dere
Wir schreiben dir, sobald eine neue geprüfte Stelle dazukommt. Höchstens eine Nachricht am Tag, und nur, wenn es tatsächlich etwas Neues gibt.
Abmelden mit einem Klick, jederzeit. Keine Weitergabe an Dritte.
Strategische Ausrichtung der divisionalen Data-, AI- und Cloud-Landschaft sowie Entwicklung einer mehrjährigen Roadmap im Einklang mit der K
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& Verantwortlichkeiten Entwicklung und Implementierung von AI- und Agenten-Lösungen von der Idee bis zum produktiven Einsatz Konzeption mode
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Die Sparda-Bank Hannover ist eine genossenschaftliche Bank.
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im Team Worum es geht Wir sind Markus und Thomas, die Gründer von replient.
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Production Go-Live in Rekordzeit: Du baust und deployst robuste Schnittstellen, Middleware und Agentic-Pipelines direkt an der Schnittstelle
Jobbeschreibung Mach mit uns Schweizer Kleinunternehmen noch erfolgreicher!
01 · Why this role exists Invest in Values.
KI-Agenten arbeiten rund um die Uhr, werden nicht müde und erledigen wiederkehrende Aufgaben zuverlässig und mit weniger Fehlern.
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Bei aktuellen Projekten dreht es sich viel um das Training von eigenen VLMs und um das, was jedes Unternehmen umtreibt: den Einsatz von Agen
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Effizienz ist für Dich kein Buzzword, sondern ein mathematisches Ziel.
Warum dieser Job gelistet ist
Eindeutige KI-Rollenbezeichnung im Stellentitel. Prüfverfahren ansehen
Train and deploy ML models for retrieval, reranking, and search relevance, such as embedding models and cross-encoders, in production - Develop models supporting crawling, data selection, and content understanding - Build and maintain the data, training and evaluation pipelines behind our models: training-data generation, evaluation sets and retraining - Contribute to and improve our ML platform: training and orchestration workflows (Kubernetes, GPU scheduling, experiment tracking, model lifecycle) and inference optimisation for low-latency serving - Work closely with engineering to integrate and deploy ML models in production services, balancing latency, quality and cost - Apply the latest architectural advances and training procedures for retrieval and LLM-integrated systems
5+ years of experience in software engineering or applied machine learning - Strong programming skills in Python and at least one of Go or C++ - Proven experience deploying ML models in production systems - Solid understanding of machine learning and modern deep learning, including transformers and embeddings - Experience building data or ML pipelines and working with workflow orchestration - Ability to define meaningful model metrics and evaluate models rigorously - Product-oriented mindset with a focus on impact and iteration Nice-to-haves: - Hands-on experience with retrieval, ranking, or recommendation - Experience with Kubernetes, distributed training or GPU scheduling, or inference optimisation (batching, quantisation, compilation) - Contributions to open-source projects, technical publications, or conference talks - Strong results on public benchmarks or leaderboards (e.g. MTEB, BEIR) or models released on Hugging Face Benefits &
Competitive compensation - Career growth and learning opportunities - Flexibility and ownership - Collaborative and innovative culture - Opportunity to work on impactful AI projects - International environment and talented teams What s it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.
About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers.
From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
We are seeking a Senior ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. Our focus is agentic search, where AI systems plan, retrieve and refine rather than just return results. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time.
This is a high-impact role working on a production system used 24x7, operating at the scale of web search.
Interesse an dieser Position?
Die vollständige Anzeige und das Bewerbungsformular liegen beim Arbeitgeber.
Zur OriginalanzeigeKI in bestehende Software und Abläufe bringen: Agenten, RAG-Systeme, Schnittstellen, automatisierte Arbeitsketten.