AI Full Stack Engineer

Company descriptionPublicis Re:Sources is the backbone of Publicis Groupe, the world's most valuable agency group. We are the only full-service, end-to-end shared service organization in the industry, enabling Groupe agencies to do what they do best: innovate and transform for their clients. Formed in 1998 as a small team to service a few Publicis Groupe firms, Publicis Re:Sources has grown to 6,000+ employees in over 55+ countries. We provide technology solutions and business services including finance, accounting, legal, benefits, procurement, tax, real estate, treasury and risk management. Our people are at the center of everything we do, bringing curiosity, collaboration, and a commitment to excellence to their work every day. We continually transform to keep pace with our ever-changing communications industry and thrive on a spirit of innovation felt around the globe. Learn more about Publicis Re:Sources and the Publicis Groupe agencies we support at Publicis Resources – A Publicis Groupe Company.OverviewPublicis 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 workflowsResponsibilitiesAI & Machine Learning IntegrationDevelop, deploy, and maintain machine learning models within production environmentsBuild AI-driven features such as classification, anomaly detection, recommendation systems, and data enrichmentIntegrate AI/ML models into applications via APIs and microservicesMonitor, evaluate, and continuously improve model performance and reliability Full Stack DevelopmentDesign and build end-to-end features across front-end and backend systemsDevelop responsive, high-performance user interfaces using modern frameworks (e.g., React, Angular)Build and maintain scalable backend services and APIs supporting data workflowsEnsure seamless integration between UI components, AI services, and data pipelines Data Platform IntegrationWork with large-scale ETL pipelines and data processing systemsIntegrate applications with structured and semi-structured data sourcesCollaborate with data engineering teams to optimize data flows and performance Cloud, DevOps & ScalabilityDeploy applications and models in cloud environmentsContribute to CI/CD pipelines, automated testing, and DevOps best practicesEnsure scalability, security, and reliability of systemsWork with containerized applications and microservices architectures Collaboration & Best PracticesPartner with UX teams to ensure AI outputs are intuitive and actionableParticipate in code reviews and enforce engineering standardsTroubleshoot and resolve issues across the full stackDocument solutions and contribute to knowledge sharingQualificationsBachelor’s degree in Computer Science, Engineering, Data Science, or related field 4+ years of experience in software engineering, full stack development, or AI/ML engineeringStrong 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 APIsFamiliarity with ETL pipelines and data platformsExperience working with SQL and NoSQL databasesKnowledge of cloud platforms (AWS, Azure, or GCP)Experience with containerization (Docker, Kubernetes is a plus)Understanding of CI/CD pipelines and DevOps practicesStrong problem-solving, debugging, and analytical skillsFluent in EnglishPreferred QualificationsExperience building AI-powered features in production applicationsFamiliarity with Databricks, data warehouses, or BI toolsExperience with MLOps practices (model lifecycle management, monitoring, versioning)Exposure to LLMs, embeddings, or generative AI applicationsExperience with microservices architectureBackground working in data-intensive or analytics platforms