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# MLOps - Data Platform Engineer

- **Company:** Noïa Labs
- **Location:** Paris, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Cloud Computing, Continuous Integration, Data Infrastructure, Distributed Computing Environment, Python (Programming Language), Metadata, Software Engineering, Data Streaming, Management of Software Versions, Data Ingestion, Machine Learning Operations, Data Pipelines
- **Published:** June 10, 2026
- **Apply:** https://fr.indeed.com/viewjob?jk=f33a32483a8c5316

## About the Role

Do you have experience in Python?, 5+ years of experience building production-grade MLOps and data infrastructure

 * Strong software engineering experience, especially in Python
 * Experience with cloud infrastructure, containers, CI/CD, and production systems
 * Experience supporting GPU workloads or distributed training
 * Experience with ML tooling for training, evaluation, tracking, or deployment
 * Strong understanding of data quality, monitoring, versioning, and reproducibility
 * Strong ownership, practical problem-solving skills, and attention to detail, Experience with time-series data such as biosignals, sensor data, audio, video, or robotics
 * Experience with real-time streaming, edge buffering, device-to-cloud synchronization, or intermittent connectivity
 * Experience in health, medical device, regulated, privacy-sensitive, or research environments

## Description

We are seeking a talented MLOps - Data Platform Engineer to build the data and ML infrastructure behind our non-invasive neural interface. You will own the systems that ingest, version, and serve large-scale neural data, making model training fast, reproducible, and scalable from dataset construction and orchestration through distributed training and deployment.

This is a hands-on role for someone who enjoys building reliable infrastructure for fast-moving ML and research teams. The role spans data pipelines, ML workflows, compute infrastructure, experiment tracking, model deployment, and internal tooling, while working closely with ML engineers, neuroscientists, software engineers, and product teams., Build and maintain the platform for neural data ingestion, processing, storage, and retrieval

 * Develop pipelines for dataset generation, training, evaluation, and deployment
 * Create tools that help ML engineers and scientists find data, run experiments, compare models, and reproduce results
 * Manage cloud and GPU compute for scalable ML workloads
 * Improve data quality, metadata, versioning, monitoring, and traceability
 * Work with software and ML teams to integrate models into the platform
 * Help define standards for reliability, reproducibility, privacy, and security


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