Senior Full-Stack Developer / AI Engineer / Data Scientist

Job Description

REQUIREMENTS

• Several years of professional experience in full-stack development
• Strong knowledge of TypeScript and JavaScript
• Confident working with React.js, Next.js, Node.js, and Nest.js
• Experience with relational databases (PostgreSQL, MySQL, MS SQL)
• Hands-on experience with container technologies (Docker, Kubernetes)
• Familiarity with REST API design and Git workflows
• Solid Python skills, including common data/ML stacks (pandas, NumPy, scikit-learn, PyTorch, or TensorFlow)
• Experience across the end-to-end lifecycle of ML models: data preparation, feature engineering, training, validation, deployment, and monitoring
• Strong understanding of statistical fundamentals (hypothesis testing, regression, classification, time series)
• Experience with modern LLM workflows: prompt engineering, RAG, embeddings, vector databases (e.g. Pinecone, Weaviate, pgvector)
• Hands-on experience in model evaluation: metrics, benchmarks, A/B testing, and systematic evaluation frameworks
• Experience with notebook-based exploration environments (Jupyter, Colab) and transitioning them into production-ready pipelines
• Familiarity with MLOps tools (e.g. MLflow, Weights & Biases, DVC) is a plus
• Experience with Claude Code and/or Codex is highly appreciated — otherwise, a strong willingness to actively learn and adopt these tools
• Excellent German (C2) and English skills (minimum C1)
• Genuine interest in generative AI and agentic systems
• Builder mentality: you deliver production-grade software, not just prototypes
• Analytical mindset: you make data-driven decisions and critically evaluate model behavior

RESPONSIBILITIES

• Develop and maintain our full-stack applications within the modern JavaScript/TypeScript ecosystem
• Design and implement scalable backend services using Node.js and Nest.js
• Build high-performance frontend solutions with React.js and Next.js
• Design and optimize relational databases (PostgreSQL, MySQL, MS SQL)
• Containerize and orchestrate services using Docker and Kubernetes
• Develop and integrate REST APIs
• Build and operate data pipelines, feature stores, and ML workflows for our agentic systems
• Develop, train, and evaluate machine learning models for marketing use cases (attribution, targeting, forecasting, anomaly detection)
• Implement Retrieval-Augmented Generation (RAG) pipelines (embeddings, vector stores, re-ranking)
• Set up and maintain LLM evaluation frameworks, prompt optimization workflows, and systematic quality benchmarks
• Apply code harnesses (especially Claude Code and Codex) throughout the daily development process — from planning and implementation to code review
• Collaborate closely with the founding team on the product architecture of agentic systems

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