Senior AI ML Engineer M/F

As a Senior AI ML Engineer with a focus on AI adoption, you will play a key role in helping ORIS integrate AI capabilities into reliable, scalable, and maintainable software solutions. While AI is an important part of the role, the primary focus is strong software engineering, system architecture, and problem-solving. Success in this position requires the ability to design and build production-grade systems that solve real business challenges, whether through AI-driven approaches or conventional software and ML solutions. You will design and develop scalable prototypes, applications and data pipelines, contributing across the full software and AI lifecycle. You will ensure solutions are secure, maintainable, and aligned with engineering best practices. Working closely with Product, Engineering, and Data teams, you will translate business requirements into practical technical solutions, integrating technologies such as generative AI and machine learning where they provide measurable value. You will also support the organization in accelerating AI adoption by developing prototypes, reference architectures, reusable implementation patterns, and technical guidelines for AI-oriented use cases. The role includes providing technical leadership for agentic AI initiatives, mentoring engineers, contributing to engineering standards, and sharing knowledge on emerging technologies and industry trends. You will work in a fast-paced, multi-cultural environment using agile methodologies and the Kanban framework, with the autonomy to evaluate and introduce technologies that improve product quality, delivery, and operational efficiency. This is a permanent position based at ORIS offices near the Part-Dieu train station in Lyon, within a collaborative team focused on building practical, high-quality software and AI capabilities for the company and its customers. You are a technology enthusiast with a strong background in software engineering and machine learning, experienced in building scalable solutions in distributed environments for real-world production use cases. This experience will support the organization in accelerating AI adoption, particularly through product innovation and the development of AI-oriented use cases. You will lead agentic AI implementation efforts by providing prototypes, reference architectures, technical guidance, and support across the organization, while also sharing knowledge on emerging technologies and industry trends. Software Engineering & Machine Learning Expertise Design and maintain CI/CD pipelines for scaled systems, especially ML and AI solutions. Deploy solutions in distributed environments such as Kubernetes and AWS. Build scalable model serving architectures (REST, streaming, event-based). Integrate services into existing observability platforms. 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. 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 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. 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. Build prototypes to validate ideas quickly using AI or exposing AI capabilities. Ensure AI features meet reliability, security, and compliance standards. Contribute to architecture decisions impacting scalability and performance. 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. 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. Evaluate emerging tools, frameworks, and foundation models. Experience and skills required : Strong background in software or data engineering Familiar with ML frameworks and models as well as AI workflows and pipelines. Experience with Docker, Kubernetes Understanding of CI/CD principles Strong knowledge of Git Hands-on experience building and integrating AI solutions into production grade systems using fine-tuned local models or third party providers ( Gemini, OpenAI, etc) Experience building AI application using langflow or similar tools Strong understanding of data structures and algorithms Knowledge of ETL pipelines, and data processing techniques (Encoding, Imputation, scaling, etc). Good understanding of vector databases Familiarity with AWS and its core services Nice to have : Familiarity with MLOps and Data-Management tools (ClearML, Mlflow, Weights and biases, tensorboard, etc) Experience with training computer vision models : Semantic segmentation, object detection, classification, etc, and understanding of their architectures (Unet, YOLO, Resnet..) Up to date with SOTA (State of the Art) architectures & research in the field Familiarity with database systems (relational and NoSQL) like PostgreSQL, MongoDb Understanding of serverless architecture and microservices Recruitment process Round 1 : Tech team / Technical Test / Round 2 : CTO & HR / Round 3 : co founder