MLops Engineer
We’re looking for an MLOps Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale.
We’re looking for an MLOps Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale.
We’re looking for a Senior Software Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale.
We re building agents that run real parts of our business, take on real work, and get better based on the results they produce.
Virtusan is a Swiss based health coaching platform for sleep, stress and recovery that works with any wearable, including our own smart ring.
Implement agent features and integrations in production codebases – APIs, websockets, tool-calling workflows – following established architecture patterns.
Wir schreiben dir, sobald eine neue geprüfte Stelle zu Remote dazukommt. Höchstens eine Nachricht am Tag, und nur, wenn es tatsächlich etwas Neues gibt.
Abmelden mit einem Klick, jederzeit. Keine Weitergabe an Dritte.
German (C1+) and English required You make sure 10+ software companies get real value out of AI.
Was Du bei uns machst Vor Ort verstehen: Mit Fachabteilung und IT herausfinden, wo Zeit verloren geht und welche Daten wo liegen Bewerten: U
Was du bei uns machst Technische Umsetzung beim Kunden: Architektur, Integration und Inbetriebnahme von KI-Lösungen (LLMs, RAG, Agentic AI,
Im Mittelpunkt steht die Weiterentwicklung des internen AI Hub auf Basis von Open WebUI.
ILP aufbauen: Du besprichst mit dem PO die nächsten Schritte der Weiterentwicklung und arbeitest erfolgreich mit den Kollegen am Aufbau der Plattform.
Agentische Systeme bauen: Du entwickelst die Agent-Architektur von paddy weiter.
Worum es bei uns wirklich geht Du identifizierst KI-Anwendungsfälle in Bankprozessen, bewertest Machbarkeit und Nutzen und überführst sie in
is mostly focused on shipping AI product features, with some AI systems and infrastructure work where needed.
RAG-Pipeline & KI: Architektur und Entwicklung der serverseitigen RAG-Pipeline, von der Query-Verarbeitung über Embedding und Vector Search
is mostly focused on shipping AI product features, with some AI systems and infrastructure work where needed.
Umsetzung von individuellen Applikationen in interessanten und herausfordernden Kundenprojekten auf Basis von ASP.NET Code (Razor/Blazor) un
is mostly focused on shipping AI product features, with some AI systems and infrastructure work where needed.
is mostly focused on shipping AI product features, with some AI systems and infrastructure work where needed.
Als AI Engineer (w/d/m) schreibst du nicht nur Notebooks und Prompts, du baust produktive GenAI-Systeme: Agenten, RAG-Pipelines, LLM-getrieb
is mostly focused on shipping AI product features, with some AI systems and infrastructure work where needed.
Du entwickelst Software mit Angular, Spring Boot, Kubernetes – für klassische und AI-basierte Lösungen Du gestaltest mit unseren Kunden ansp
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Eindeutige KI-Rollenbezeichnung im Stellentitel. Prüfverfahren ansehen
We’re looking for an MLOps Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale. Who We Are At Seedtag, our mission is to transform advertising by proving that effectiveness and user privacy can truly coexist. As the leading Neuro-Contextual Advertising Company, we combine Artificial Intelligence, Natural Language Processing, Computer Vision, and neuroscience to understand not only what content is about, but how it makes people feel and what they intend to do next.
Our proprietary AI, Liz, enables brands to connect with audiences across the open web and Connected TV without cookies or user tracking. Founded in 2014 by two ex-Googlers, Seedtag has grown to 500+ Seedtaggers in 17 countries, backed by €250M in funding, and operates today as a global ad-tech leader. If you enjoy solving complex engineering challenges and building AI-driven systems at scale, you’ll feel right at home here.
Your Challenge As our MLOps Engineer, you will help scale and maintain the infrastructure behind Liz, Seedtag s contextual AI, ensuring reliable, efficient, and low-latency ML model deployment across the open web and CTV. Your main responsibilities will include: - ML Model Deployment: Deploy, maintain, and optimise ML models in production, ensuring performance, reliability, and latency standards. - Data Pipelines: Build and improve pipelines for dataset generation, model training, validation, and performance analysis. - Model Observability: Implement monitoring, dashboards, drift detection, and alerting to ensure model quality and stability. - MLOps Infrastructure: Improve CI/CD pipelines, model versioning, reproducibility, and infrastructure automation. - Performance & Cost Optimisation: Identify opportunities to reduce serving costs and improve efficiency without compromising model quality. - AI Automation: Develop internal automations and agentic integrations to streamline ML workflows. - Cross-Functional Collaboration: Work closely with Data, Platform, and Backend teams to troubleshoot production issues and improve ML systems. Our Core Values Outcome over Output We measure success by impact and value, not by volume of features or lines of code.
Failure Is Allowed, Learning Is a Must Experimentation is key to innovation. We test early, iterate often, and learn fast. We Are All Scouts We take ownership and leave things better than we found them.
We Are Data-Driven Data informs our decisions and helps us continuously improve our systems and results. Tech Stack You ll work with the technologies powering LizCore, our contextual AI infrastructure, supporting real-time ML model serving across the open web and CTV. Our stack includes: - Languages: Python, Go. - Cloud & Infrastructure: GCP, Vertex AI, Kubernetes, Terraform. - MLOps & Monitoring: MLflow, Weights & Biases, Evidently, Prometheus, Grafana. - Data & Messaging: Kafka, Redis, Bigtable, MongoDB. - AI & Machine Learning: NLP, embeddings, ranking models, MCP, agentic workflows. - Engineering Practices: CI/CD, model versioning, automated testing, reproducibility.
What You’ll Need to Succeed - 2–4 years of experience building, deploying, and maintaining ML systems in production. - Strong Python skills, including APIs, asynchronous programming, testing, and clean architecture. - Hands-on experience with ML model development and production deployment. - Solid understanding of MLOps practices, including CI/CD, model versioning, reproducibility, and monitoring. - Experience with ML observability, including performance metrics, dashboards, and alerting. - Knowledge of distributed systems, microservices, Kubernetes, and cloud infrastructure. - Strong troubleshooting skills across models, data pipelines, APIs, and infrastructure. - Proactive, curious, and autonomous, with the ability to own technical components end to end. Why Join Seedtag? - A key moment of growth with real ownership and global impact. - Flexible work model with 100% remote or hybrid options. (Remote contracts available in Spain, Italy, UK, Belgium, Netherlands, France, and Germany.) - Continuous learning through a learning platform and optional language classes. - A supportive, trust-based culture that values well-being. - Team activities, offsites, and opportunities to connect beyond work.
Additional Perks - Home office setup budget up to €1,000 - Paid trips to our HQ in Madrid - MacBook Pro M3 Ready to Join the Seedtag Adventure? At Seedtag, we create an environment where everyone can thrive. If you need accommodations during the hiring process, let us know and we’ll ensure a positive experience.
Send us your CV and let’s build the future of neuro-contextual advertising together.
Interesse an dieser Position?
Die vollständige Anzeige und das Bewerbungsformular liegen beim Arbeitgeber.
Zur OriginalanzeigeHier stehen ausschließlich Stellen, die ortsunabhängig ausgeschrieben sind. Anzeigen, die Homeoffice an einzelnen Tagen erlauben, laufen unter Hybrid und nicht hier, denn wer ortsunabhängig sucht, will nicht pendeln. Diese Unterscheidung trifft kijobs selbst: Die Quellen weisen sie nicht aus, erkannt wird sie aus Titel, Ortsangabe und Anzeigentext, und im Zweifel ordnen wir vorsichtig ein, lieber hybrid als remote. Beachte dabei, dass remote selten weltweit heißt. Viele Arbeitgeber verlangen einen Wohnsitz im selben Land oder zumindest innerhalb der EU, und das steht meist erst im Anzeigentext.