Senior AI Engineer

About

As a Senior AI Engineer, you will play a critical role in the ORIS organization, leading the design, development, and deployment of production-grade AI systems that drive measurable business and product impact. You will take ownership of end-to-end AI initiatives, from data architecture and model development to MLOps pipelines and system reliability, ensuring our AI capabilities scale securely and efficiently.

You will collaborate with cross-functional teams, including Product, Engineering, and Data, to translate complex business requirements into robust AI solutions, integrating generative AI, large language models, and advanced machine learning into our platform. You will also mentor engineers, drive adoption of best practices, and shape the AI strategy across the company.

This role offers the opportunity to work in a fast-paced, multi-cultural environment, embracing agile principles and leveraging the Kanban framework, while exploring cutting-edge AI technologies and frameworks. You will have the freedom to innovate, introduce state-of-the-art AI methods, and ensure responsible, high-quality, and reliable AI systems.

This is a permanent contract based at ORIS offices near the Part-Dieu train station in Lyon, where you will join a collaborative and ambitious team driving the future of AI at ORIS.

Your missions :

AI Data Engineering & Foundation Building

• Design and implement scalable data architectures to support AI workloads (batch & real-time).
• Build reliable data ingestion pipelines from APIs, databases, event streams, imagery systems, and third-party services.
• Ensure data quality through validation, versioning, lineage tracking, and monitoring.
• Implement data governance practices aligned with privacy and security standards (GDPR, SOC2, ISO 27001).
• Collaborate with DevOps to maintain secure and reproducible data environments.

AI Systems Design & Model Engineering

Design, develop, fine-tune, and evaluate ML & Deep Learning models including:

• Computer Vision, NLP, Large Language Models (LLMs), Generative AI systems, Multimodal models
• Implement RAG (Retrieval-Augmented Generation) architectures where applicable.
• Define appropriate evaluation frameworks (offline & online metrics).
• Optimize models for inference performance (latency, cost, scalability).
• Integrate foundation models (OpenAI, Google Gemini, open-source LLMs, etc.) responsibly and strategically.

AI Product Integration & Engineering Collaboration

• Collaborate with Product & Engineering teams to translate business problems into AI system designs.
• Define APIs and service layers for AI-powered features.
• Contribute production-ready Python/TypeScript code integrated into the SaaS architecture, especially backend.
• Ensure AI features meet reliability, security, and compliance standards.
• Contribute to architecture decisions impacting scalability and performance.

AI Governance, Security & Responsible AI

• Implement AI governance frameworks (bias detection, explainability, fairness checks).
• Ensure compliance with evolving AI regulations (EU AI Act, etc.).
• Lead model documentation and traceability standards.
• Evaluate risks related to generative AI (hallucination, data leakage, prompt injection).

AI Leadership & Innovation

• Identify high-impact AI opportunities aligned with product strategy.
• Take responsibility to shape technical AI roadmap initiatives.
• Evaluate emerging tools, frameworks, and foundation models.
• Mentor engineers and promote AI engineering best practices.
• Foster an AI-first engineering culture.

Models development, validation and deployment

• Develop and train machine learning models of different types (computer vision, NLP, …) for various tasks (classification, regression, clustering, object detection, text generation, …)
• Establish adequate models’ performance metrics according to the task (MAE, Precision, Accuracy, IoU, etc)
• Validate and optimize the models for inference
• Deploy the models and make them accessible to production systems
• Build and maintain data pipelines to provide end to end IA based solutions.

AI Platform & MLOps Leadership

• Design and maintain CI/CD pipelines for ML systems.
• Implement model versioning, experiment tracking, and reproducibility.
• Deploy models using containerized environments (Docker/Kubernetes).
• Build scalable model serving architectures (REST, streaming, event-based).
• Monitor model drift, performance degradation, and system health.
• Establish automated retraining pipelines.

Strategic Guidance and continuous improvement

• Provide guidance based on data analysis, identifying opportunities for AI based solutions to improve our product and services.
• Stay updated on the latest data science methodologies, tools and technologies.
• Share knowledge with the team and promote a culture of AI.