Company description Re:Sources is the backbone of Publicis Groupe, the world’s third-largest communications group. Formed in 1998 as a small team to service a few Publicis Groupe firms, Re:Sources has grown to 4,000+ people servicing a global network of prestigious advertising, public relations, media, healthcare and digital marketing agencies. We provide technology solutions and business services including finance, accounting, legal, benefits, procurement, tax, real estate, treasury and risk management to help Publicis Groupe agencies do what they do best: create and innovate for their clients. In addition to providing essential, everyday services to our agencies, Re:Sources develops and implements platforms, applications and tools to enhance productivity, encourage collaboration and enable professional and personal development. We continually transform to keep pace with our ever-changing communications industry and thrive on a spirit of innovation felt around the globe. With our support, Publicis Groupe agencies continue to create and deliver award-winning campaigns for their clients. Overview Publicis Groupe is building a modern, scalable data and analytics ecosystem powered by AI-driven intelligence. We are seeking an AI Full Stack Engineer to design, develop, and integrate end-to-end solutions that combine advanced machine learning capabilities with robust, user-facing applications. This role sits at the intersection of AI/ML, software engineering, and data platforms, enabling the development of intelligent features that enhance data ingestion, discovery, automation, and insights across the platform. You will work closely with data engineers, UX designers, product teams, and DevOps engineers to deliver scalable, high-performance solutions that bring AI into real user workflows Responsibilities AI & Machine Learning Integration Develop, deploy, and maintain machine learning models within production environments Build AI-driven features such as classification, anomaly detection, recommendation systems, and data enrichment Integrate AI/ML models into applications via APIs and microservices Monitor, evaluate, and continuously improve model performance and reliability Full Stack Development Design and build end-to-end features across front-end and backend systems Develop responsive, high-performance user interfaces using modern frameworks (e.g., React, Angular) Build and maintain scalable backend services and APIs supporting data workflows Ensure seamless integration between UI components, AI services, and data pipelines Data Platform Integration Work with large-scale ETL pipelines and data processing systems Integrate applications with structured and semi-structured data sources Collaborate with data engineering teams to optimize data flows and performance Cloud, DevOps & Scalability Deploy applications and models in cloud environments Contribute to CI/CD pipelines, automated testing, and DevOps best practices Ensure scalability, security, and reliability of systems Work with containerized applications and microservices architectures Collaboration & Best Practices Partner with UX teams to ensure AI outputs are intuitive and actionable Participate in code reviews and enforce engineering standards Troubleshoot and resolve issues across the full stack Document solutions and contribute to knowledge sharing Qualifications Bachelor’s degree in Computer Science, Engineering, Data Science, or related field 4+ years of experience in software engineering, full stack development, or AI/ML engineering Strong programming skills in Python and at least one backend language (Node.js, Java, .NET, Go.) Experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn) Experience with front-end frameworks (React, Angular, or similar) Experience designing and consuming RESTful APIs Familiarity with ETL pipelines and data platforms Experience working with SQL and NoSQL databases Knowledge of cloud platforms (AWS, Azure, or GCP) Experience with containerization (Docker, Kubernetes is a plus) Understanding of CI/CD pipelines and DevOps practices Strong problem-solving, debugging, and analytical skills Fluent in English Preferred Qualifications Experience building AI-powered features in production applications Familiarity with Databricks, data warehouses, or BI tools Experience with MLOps practices (model lifecycle management, monitoring, versioning) Exposure to LLMs, embeddings, or generative AI applications Experience with microservices architecture Background working in data-intensive or analytics platforms