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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer, Biologics Discovery - **Company:** Johnson U0026 Johnson - **Location:** Madrid, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Automation of Tests, Cloud Computing, Continuous Integration, Data Infrastructure, Python (Programming Language), Machine Learning, Release Management, Software Engineering, Management of Software Versions, AI Platforms, Information Technology, Deployment Automation, Machine Learning Operations, Data Pipelines, Automation Anywhere - **Published:** September 29, 2026 - **Apply:** https://www.buscojobs.com.es/senior-machine-learning-engineer-biologics-discovery-en-madrid-ID-373392121 ## About the Role products and AI/ML workflows * Enable reproducible training, fine-tuning, evaluation, and deployment capabilities for ML scientists and AI agents * Contribute to standardization and best practices to accelerate transition from experimentation to production * Support security, access control, AI governance, documentation, and cost management across AI/ML solutions Responsabilidades * Degree in Computer Science, Engineering, Data Science, Machine Learning, or related computational field * 4+ years of experience operationalizing and scaling AI/ML solutions in production * Strong Python proficiency for AI/ML workflows across training, fine-tuning, deployment, and serving * Experience with cloud infrastructure and modern data platforms for AI/ML workloads * Experience with model registries, experiment tracking, and ML lifecycle tools (e.g., MLflow, Weights u**** Biases) * Experience implementing production AI/ML practices: versioning, deployment automation, CI/CD, automated testing ## Description Experteer Overview In this Senior ML Engineer role, you will operationalize AI/ML capabilities for Biologics Discovery, bridging data science with discovery scientists.You'll implement scalable data pipelines, governance, and lifecycle practices to move models from prototype to production.You'll partner with researchers and engineers to enable reliable, production-grade AI workflows and accelerate scientific discovery.This position emphasizes reliability, reproducibility, and automation within a leading healthcare innovator.Compensaciones / Beneficios * Build and operate scalable pipelines delivering model-ready data to ML, generative AI, and agentic workflows * Enable closed-loop scientific learning by capturing and provisioning new data for downstream modeling and evaluation * Establish production-grade deployment, serving, monitoring, access control, and lifecycle management for ML/AI across environments * Implement versioning and reproducible releases with traceability across the ML lifecycle * Set up monitoring, observability, alerting, and performance management for ML workflows and AI services * Develop automated workflows for testing, release management, and environment management across AI/ML solutions * Scale AI capabilities to handle larger data volumes and more autonomous discovery workflows * Monitor production model behavior, data quality, drift, latency, and resource utilization * Partner with data scientists and domain experts to define reliable integration patterns between data products and AI/ML workflows * Enable reproducible training, fine-tuning, evaluation, and deployment capabilities for ML scientists and AI agents * Contribute to standardization and best practices to accelerate transition from experimentation to production * Support security, access control, AI governance, documentation, and cost management across AI/ML solutions Responsabilidades * Degree in Computer Science, Engineering, Data Science, Machine Learning, or related computational field * 4+ years of experience operationalizing and scaling AI/ML solutions in production * Strong Python proficiency for AI/ML workflows across training, fine-tuning, deployment, and serving * Experience with cloud infrastructure and modern data platforms for AI/ML workloads * Experience with model registries, experiment tracking, and ML lifecycle tools (e.g., MLflow, Weights u**** Biases) * Experience implementing production AI/ML practices: versioning, deployment automation, CI/CD, automated testing, observability, containers, orchestration, scalable compute * Strong software development and automation practices and ability to partner with data scientists and domain experts Requisitos principales * medical, dental, and vision insurance * life and disability insurance * retirement plan (pension) and 401(k) * vacation and personal time off * paid holidays and sick leave ## Related Videos - [This App Reached 10,000 Users in One Week. Here's How.](https://www.wearedevelopers.com/videos/100329-this-app-reached-10-000-users-in-one-week-here-s-how) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Green Cloud Computing](https://www.wearedevelopers.com/videos/592-green-cloud-computing) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML ](https://www.wearedevelopers.com/videos/78-leverage-cloud-computing-benefits-with-serverless-multi-cloud-ml) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)