Senior AI ML Engineer M/F (RQTH) - Permanent contract

ORIS; Lyon; Hybride; EUR 90 000 - 130 000; Plein temps

Résumé du poste

ORIS seeks a Senior AI ML Engineer to lead the design and deployment of scalable AI-powered software solutions. You will drive AI adoption while delivering robust, production-grade systems and data pipelines.

You will collaborate with Product, Engineering, and Data teams, mentoring engineers and shaping the AI roadmap. The role emphasizes system architecture, security, and maintainable software practices in a fast-paced environment.

Qualifications

• Strong background in software or data engineering.
• Familiar with ML frameworks and AI workflows and pipelines.
• Experience with Docker, Kubernetes.
• Understanding of CI/CD principles.
• Strong knowledge of Git.
• Hands-on experience building production-grade AI solutions.

Responsabilités

• Design and build production-grade AI/ML systems and pipelines.
• Develop scalable prototypes, apps and data pipelines.
• Collaborate with Product, Engineering, and Data teams.
• Mentor engineers and promote best practices.
• Define APIs and service layers for AI-powered features.
• Ensure reliability, security, and compliance of AI features.

Connaissances

Software Engineering

Data Engineering

Docker

Kubernetes

CI/CD

Git

AI Production

LangFlow

OpenAI / Gemini

AWS Cloud

Data Structures & Algorithms

ETL & Data Processing

Vector Databases

Outils

LangFlow

OpenAI

Gemini

Description du poste

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.

Required :Strong background in software or data engineeringFamiliar with ML frameworks and models as well as AI workflows and pipelines.Experience with Docker, KubernetesUnderstanding of CI/CD principlesStrong knowledge of GitHands-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 toolsStrong understanding of data structures and algorithmsKnowledge of ETL pipelines, and data processing techniques (Encoding, Imputation, scaling, etc).Good understanding of vector databasesFamiliarity with AWS and its core servicesNice to haveFamiliarity 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 fieldFamiliarity with database systems (relational and NoSQL) like PostgreSQL, MongoDbUnderstanding of serverless architecture and microservices