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AI Full Stack Engineer

Publicis Re:Sources · San Jose

Senior 🇬🇧 English
Python Node.js Java .NET Go TensorFlow PyTorch scikit-learn React Angular SQL NoSQL AWS Azure GCP Docker Kubernetes CI/CD ETL

Description du poste

About the role

Re:Sources, the technology arm of Publicis Groupe, is building a modern, scalable data and analytics ecosystem powered by AI-driven intelligence. It is looking for an AI Full Stack Engineer who designs, develops and integrates end-to-end solutions combining machine learning with robust, user-facing applications. The role sits at the intersection of AI/ML, software engineering and data platforms, working with data engineers, UX designers, product teams and DevOps engineers.

Key responsibilities

  • Develop, deploy and maintain machine learning models in production, and build AI features such as classification, anomaly detection, recommendation systems and data enrichment
  • Integrate AI/ML models into applications via APIs and microservices, and monitor and improve model performance
  • Design and build full-stack features, including responsive front-end interfaces (e.g. React, Angular) and scalable back-end services and APIs
  • Work with large-scale ETL pipelines and integrate applications with structured and semi-structured data sources
  • Deploy applications and models in the cloud, contribute to CI/CD pipelines and automated testing, and work with containerized applications and microservices
  • Partner with UX teams, take part in code reviews, troubleshoot issues across the stack and document solutions

Required profile

  • Bachelor's degree in Computer Science, Engineering, Data Science or a related field
  • 4+ years of experience in software engineering, full stack development or AI/ML engineering
  • Strong problem-solving, debugging and analytical skills; fluent in English

Required skills

  • Python and at least one backend language (Node.js, Java.NET, Go)
  • Machine learning frameworks (TensorFlow, PyTorch, scikit-learn) and front-end frameworks (React, Angular)
  • RESTful APIs, SQL and NoSQL databases, and familiarity with ETL pipelines
  • Cloud platforms (AWS, Azure, GCP), Docker, and CI/CD and DevOps practices
  • Preferred: production AI features, Databricks or BI tools, MLOps, LLMs/embeddings/generative AI, microservices

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Publicis Re:Sources

San Jose