Job Information
Publicis Groupe AI Full Stack Engineer in San Jose, Costa Rica
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