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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Operations Architect - **Company:** Inbrain Neuroelectronics - **Location:** Spain - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Clinical Data Repository, Continuous Integration, Python (Programming Language), Management of Software Versions, Fast Healthcare Interoperability Resources, Model Validation, Kubernetes, Performance Monitor, Machine Learning Operations, DO-178B, Software Version Control - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/machine-learning-operations-architect-with-medical-device-experience-inbrain-neuroelectronics-8978285 ## About the Role * Bachelor's or Master's degree in Computer Science, Mathematics, Physics, Electrical Engineering or a related field. * At least 4-5 years of Hands-on ML Ops engineering experience (preferably in medtech industry): model versioning/registries (MLFlowor equivalent), CI/CD for ML, containerized deployment (Kubernetes), and workflow orchestration (e.g.Argo or equivalent). * Strong Architecture experience within industry setting (only academia or traineeship will not be considered). * Proven experience shipping ML into a regulated or safety-critical environment (medtech - priority, automotive, aerospace), has designed systems to automatize design-control processes (e.g.IEC 62304, ISO 13485, DO-178C, ISO 26262) and has used them from the inside. * Strong infrastructure-as-code skills and comfort directly supporting data scientists to productionize research code written in Python. * Fluency in English required (English is company language). * Direct experience with continuous/adaptive learning systems under a change-control or PCCP-like framework, delivering personalized evolving models in contrast to single model validation for universal use releases. * Comfortable operating at the intersection of engineering and regulatory/clinical teams, translating design-control requirements into concrete technical implementation. * Pragmatic about scope and sequencing - able to prioritize the governance/compliance work. * Strong written documentation skills, given the role's heavy emphasis on producing auditable change-control and validation records. Nice to have: * Experience with FDA's Predetermined Change Control Plan (PCCP) guidance or equivalent adaptive-SaMD regulatory frameworks (e.g.through an imaging/diagnostic AI company that has filed one). * Background in adaptive neurostimulation, closed-loop deep brain stimulation or closed-loop diabetes management with insulin pumps. * Familiarity with clinical data standards (FHIR, SNOMED CT, NWB/BIDS) and ISO 14155/MDR/ICH E9 evidence frameworks. ## Description * Design and build the model versioning, lineage, and validation-evidence system that ties every trained model to its training data, the data source origin, risk management, and clinical evidence. * Draft and maintain a Predetermined Change Control Plan (PCCP)-style framework defining permitted modification types, bounds, and automated re-validation protocols for adaptive models, in collaboration with clinical, regulatory, product and software stakeholders. * Design and configure the CI/CD pipeline for training, validating, and promoting models, running on infrastructure provided by the Software team, with promotion gates across Development, Testing, Acceptance and Production (DTAP) tiers. * Build drift and performance monitoring for models and define triggers for scheduled vs. drift-triggered retraining as part of post-market surveillance activity. * Deployed and maintained processes and services versioning and tracing them against design-control processes. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Fireside Chat: Deep Learning, Deep Impact: Harnessing AI for Language Innovation](https://www.wearedevelopers.com/videos/612-fireside-chat-deep-learning-deep-impact-harnessing-ai-for-language-innovation) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Exploring 5 Key Applications of AI Abundance with Blockchain Assurance](https://www.wearedevelopers.com/videos/971-exploring-5-key-applications-of-ai-abundance-with-blockchain-assurance) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) ## Related Articles - [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) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)