Your mission 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 worlds 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.

As a Machine Learning Operations Architect, you will architect and own the model lifecycle governance and deployment 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. Design and build the model versioning, lineage, and validation-evidence system that ties every trained model to its training data,the data source origin,riskmanagement, and clinical evidence.

Draft andmaintaina 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,productand softwarestakeholders. Design and configure the CI/CD pipeline for training,validating, and promoting models,running on infrastructure provided by the Software team,with promotion gatesacross Development, Testing, Acceptance and Production (DTAP) tiers. Bachelor's orMaster's degree in Computer Science, Mathematics, Physics, ElectricalEngineeringora relatedfield.

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.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 designedsystems toautomatizedesign-control processes (e.g.Strong infrastructure-as-code skills and comfort directly supporting data scientists toproductionizeresearch 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 modelsin 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. Strong written documentation skills, given the role's heavy emphasis on producing auditable change-control and validation records.

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). Familiarity with clinical data standards (FHIR, SNOMED CT, NWB/BIDS) and ISO 14155/MDR/ICH E9 evidence frameworks.

Meaningful Work Impact: Our projects are not only exciting and challenging but also have a positive impact on the industry and society as a whole.

Private Health Insurance

Training bonus for professional development and access to Udemy platform Hybrid working modality

📌 Machine Learning Operations Architect (with medical device experience) (Bellprat)
🏢 Inbrain Neuroelectronics
📍 Bellprat