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ML/MLOps engineer taking models from experimentation to production, working with GCP Vertex AI, Python, BigQuery, and data pipelines on a hybrid basis in Madrid.
12-month hybrid internship building scalable data pipelines and AI/GenAI applications on the Data Platform & MLOps team at a large MedTech company in Madrid.
The MLOps Engineer will work on a long-term project in Spain, focusing on the configuration, administration, and optimization of Stratio components. The role requires a strong background in STEM and specific experience with the Stratio platform.
Lead data analytics consulting projects for financial services clients, mentoring teams and driving AI/ML adoption to deliver measurable business outcomes.
Lead Capco’s UK Data & Analytics practice, driving AI-enabled solutions and growth for financial services clients while managing teams and client relationships.
Lead AI-driven data and analytics programs for top-tier financial institutions, designing and delivering scalable machine learning solutions while mentoring teams and shaping AI strategy.
The Senior MLOps Engineer will manage the operational lifecycle of production machine learning models in a biomedical context, focusing on deployment, monitoring, and scaling infrastructure. The role involves working with distributed training technologies and cloud-native tools to support drug discovery research.
Build and operate Upstart's ML and simulation platform infrastructure—model training, feature engineering, inference, and marketplace simulation—using Python, Kotlin, AWS, Spark/Databricks, and MLOps tooling.
Build and deploy large-scale biomedical AI models that accelerate drug discovery, turning research into production-ready systems.
Build and scale secure, low-latency authentication services in Go/Python that power voice-based identity verification, integrating AI/ML signals for fraud detection across AWS/GCP.
This is an apprenticeship role for a Data Scientist/AI Engineer to develop machine learning models and predictive solutions within a financial risk department. The position involves the full data science lifecycle, from data exploration and feature engineering to model deployment and monitoring.
Senior Forward Deployed Software Engineer on ServiceNow's Applied AI team, building and deploying production LLM-powered applications end-to-end for strategic enterprise customers in London, spanning backend services, orchestration pipelines, and front-end integrations.
At PDR.cloud , we are digitalizing the workshop of the future. Since 2018, we have been developing a cloud-based SaaS platform from Berlin that helps automotive repair shops manage complex damage processes more easily,…
Designs and deploys AI/ML solutions (NLP, predictive modeling, generative AI) on AWS Bedrock, integrating LLMs and RAG for enterprise use cases while ensuring scalability, security, and ethical AI practices.
Leads platform, SRE, and MLOps teams to build and scale cloud-native infrastructure for an AI-driven climate-tech startup, enabling reliable, secure systems for wildfire risk prediction and grid resilience.
Machine Learning Engineer at Cinder building classification pipelines, confidence cascading, and model training/serving infrastructure for a trust & safety platform, using Python, PyTorch, scikit-learn, XGBoost, and LLMs.
Build and scale ML-driven dispute optimization systems, including training, deploying, and monitoring models, plus real-time data pipelines and feature stores for Checkout.com’s fintech platform.
True Zero Technologies, a veteran-owned small business, was founded on the principle that the purposeful enablement of people and technology in an organization directly ties to the quality of its outcomes. True Zero…
Builds, deploys, and optimizes AI/ML systems for healthcare diagnostics, focusing on scalable, compliant, and production-ready ML services with Python, PyTorch, and cloud platforms.
The Software Engineer III will develop and maintain scalable AI services and computational imaging pipelines for medical diagnostic software. The role involves designing system architecture, ensuring production reliability, and collaborating with cross-functional teams using Python, PyTorch, and cloud infrastructure.
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