We are scientists, doctors, techies and humanity lovers, with the mission of pioneering real time precision neurology to cure brain-related disorders. INBRAIN harnesses the extraordinary material properties of Graphene, the world’s thinnest and nobel-prize winning material, to build high resolution neural systems. Our mission is to decode and modulate neural networks to restore people's lives. The Principal MLOps Architect will design and own the end-to-end model lifecycle architecture and governance framework that enables INBRAIN's data scientists and ML engineers to take adaptive neurostimulation and Brain-Computer Interface models from research into regulated clinical products . The role defines how models are developed, versioned, validated, packaged, released, monitored and updated across cloud, smartphone and embedded runtimes . It establishes the MLOps pipelines, validation gates, traceability and evidence required to ensure that every model release is reproducible, controlled and aligned with the intended clinical use. It works closely with the Software team, which owns and maintains the underlying model runtime infrastructure. The role bridges MLOps engineering with medical-device design-control practice : it defines how models are versioned, validated, and promoted, and builds the change-control framework (aligned with a PCCP-style approach) that governs how adaptive, continuously-learning models can be safely updated post-deployment. This is a foundational, cross-functional role within the Data Intelligence team. It works closely with Data Scientists, ML Engineers and Data Engineers to establish reproducible model development and delivery practices. The role partners with Clinical, Regulatory and Product teams to translate clinical objectives,



intended use and regulatory requirements into model lifecycle controls . It collaborates with Systems Engineering to ensure models meet system-level requirements and with Software Engineering to define deployment interfaces, runtime requirements and release processes across cloud, smartphone and embedded environments. Design and own the model lifecycle traceability framework , linking every model version to its source code, training datasets, data provenance, preprocessing and feature definitions, training configuration, evaluation results, risk assessments and applicable clinical evidence. Lead the technical implementation of a controlled model-change framework, aligned with applicable PCCP requirements and design-control processes. Partner with Clinical, Regulatory, Product and Software teams to ensure that model updates remain within approved safety and performance limits. Own 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. Define and implement the model monitoring and post-deployment surveillance architecture , covering model performance, data and concept drift, input quality, operational behavior and relevant safety indicators. Establish thresholds, alerting,



investigation workflows and criteria for scheduled or event-triggered retraining and controlled model updates, in collaboration with Clinical, Regulatory and Software teams. Establish and maintain traceability between model development activities, software and model artifacts, verification and validation results, risk management records, approvals and releases. Define the model packaging and deployment standards for cloud, smartphone and embedded runtimes , including model format compatibility, resource constraints, quantization or optimization requirements, runtime dependencies, version compatibility, deployment verification and rollback mechanisms. Establish clear integration contracts with Software Engineering for model handover and execution. Bachelor's or Master's degree in Computer Science , Mathematics, Physics, Electrical Engineering or a related field. At least +7 years of hands-on MLOps engineering experience (preferably in medtech industry), designing and implementing production MLOps architectures, including model registries and versioning, experiment tracking, reproducible training pipelines, automated model validation, CI/CD for ML, containerized deployment and workflow orchestration (e.g. Strong experience with infrastructure-as-code, automation and production Python workflows. Demonstrated ability to work directly with Data Scientists and ML Engineers to transform experimental research code into reproducible, tested, maintainable and production-ready ML pipelines. 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. Proven experience shipping ML into a regulated or safety-criti

📌 Azure Architect (España)
🏢 Inbrain Neurolectronics
📍 España