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# AI-ML Principal Software Engineer

- **Company:** VIAVI Solutions France SAS
- **Location:** Saint-Étienne, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, C++ (Programming Language), Continuous Integration, Data Visualization, Python (Programming Language), Machine Learning, NumPy, Software Product Management, Software Tools, Signal Processing, Smart Devices, SQL Databases, Test Data, Management of Software Versions, Supervised Learning, Data Processing, Real Time Systems, Convolutional Neural Networks, Keras, Pandas, Scikit Learn, Kubernetes, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Virtual Agents, Unsupervised Learning
- **Published:** September 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=86b029f91e1dedb8

## About the Role

* Master's engineering degree in computer science, Electrical Engineering, Data Science, Artificial Intelligence, or a closely related technical field.
 * At least 5 years of AI/ML experience in an industrial R&D environment.
 * Collaborative Team Player: Exceptional ability to work effectively and collaboratively within diverse, cross-functional teams with international stakeholders in English and French.
 * Communication & Presentation: Excellent written and verbal English & French communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders and prepare clear, concise documentation.
 * Problem-Solving & Critical Thinking: Demonstrated strong analytical and problem-solving skills, with a proactive approach to identifying and resolving technical challenges.

## Description

Based within the R&D department in Saint-Étienne (60 people) and reporting to the System Group Manager, you are experienced, passionate, and a results-oriented Artificial Intelligence and Machine Learning (AI/ML) Engineer ready to drive innovation in the critical field of Fiber Test and Measurement., In this role, you will transform complex network testing challenges into innovative AI-driven solutions across the entire AI/ML lifecycle.

You will:

 * Generate and rigorously assess new AI/ML concepts for fiber test and measurement applications.
 * Design, build, and rigorously validate AI/ML models for fiber test and measurement applications, writing the code yourself.
 * Apply signal processing and statistical analysis techniques (e.g., filtering, spectral analysis, noise characterization) directly to fiber network test data to extract features and inform model design.
 * Own the AI/ML and signal processing pipeline end-to-end, from data collection and pre-processing through model development, proofs-of-concept (PoCs) and Minimum Viable Products (MVPs) validation, and integration into production fiber testing and analytics platforms.
 * Collaborate with cross-functional, international teams to shape the future of optical network intelligence., Software Skills: Strong programming foundation across data science and production software development, with comfort using AI tools to accelerate workflows.
 + Python (Pandas, NumPy, scikit-learn).
 + Java (fiber monitoring product software).
 + C++ (embedded product software).
 + AI agent tools (e.g., Claude Code) for development, testing, documentation.
 * Signal Processing: Practical experience applying signal processing and statistical techniques to test and measurement data.
 + Filtering, spectral analysis, noise characterization.
 + Time series analysis.
 + Statistical modeling.
 + Optimization.
 * AI/ML Pipeline: End-to-end experience across the ML lifecycle, from data handling through model development, deployment, and communication of results.
 + Data handling: SQL, structured/unstructured data sources.
 + ML frameworks: TensorFlow, PyTorch, Keras.
 + Core algorithms: strong understanding of modern convolutional neural networks and transformer architectures (ConvNext, ResNet, U-Net, DETR), supervised learning (regression, classification), unsupervised learning (contrastive learning, clustering, dimensionality reduction).
 + Optimization & hyperparameter tuning (e.g., Optuna, Ray Tune, grid/random search).
 + MLOps: versioning, testing, CI/CD, model lifecycle management (MLflow, Kubeflow, ONNX).
 + Deployment: cloud environments, edge devices for real-time applications (Sagemaker, TensorRT).
 + Data visualization for communicating model performance and results to both technical and non-technical audiences (MLFlow, TensorBoard).