Fullstack Gen AI Developer

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group has over 360,000 team members globally in more than 50 countries with 55-year heritage and deep industry expertise. Capgemini addresses the entire breadth of client business needs from strategy and design to operations, focusing on cloud, data, AI, connectivity, software, digital engineering and platforms.

• Passion for Generative AI and fullstack development with experience delivering AI-powered web applications.
• Over 2 years of software development experience in both frontend and backend environments.
• Strong programming skills in Python and JavaScript/TypeScript with experience in frameworks such as FastAPI, Flask, Node.js, React, Next.js.
• Working knowledge of LLMs, prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
• Experience connecting to GenAI APIs and managing vector databases like FAISS, Pinecone, Weaviate.
• Familiarity with cloud platforms (AWS, Azure, GCP) and CI/CD pipelines for AI-driven applications deployment.
• Understanding of data pipelines, embeddings, and model-serving infrastructure.
• Strong communication skills in English; German or Polish are a plus.

Nice to have:

• Experience with LangChain, LangGraph, OpenAI Function Calling, or custom agent frameworks.
• Familiarity with Vercel AI SDK, Next.js Server Actions, or other GenAI-focused web stacks.
• Background in UX for AI applications, multimodal systems, or real-time AI interfaces (chat, co-creation tools).
• Experience building end-to-end prototypes or AI SaaS products from concept to production.

• Design, build, and deploy fullstack Generative AI applications integrating advanced LLMs, image, and multimodal models into scalable products.
• Develop and maintain frontend interfaces (using React, Next.js) and backend systems (with Python, Node.js, or similar) powering GenAI features.
• Implement AI-driven workflows including prompt orchestration, retrieval-augmented generation (RAG) pipelines, and real-time model interactions.
• Integrate foundation models and APIs (such as OpenAI, Anthropic, Hugging Face, Stability) into backend services and interactive user experiences.
• Collaborate with data scientists and product teams to translate business requirements into AI-enabled digital experiences.
• Ensure system scalability, performance, and security leveraging cloud infrastructure and MLOps best practices.
• Stay updated with GenAI innovations by experimenting with new models, frameworks, and architectures.