Head of Applied AI (m/f/d)

In this role you will lead enmacc’s shift to an AI-first trading platform, unifying data talent into a high-velocity Data & AI subdepartment. You drive both classical data science for market intelligence and modern LLM/Generative AI for product automation. You bridge raw energy data with intelligent features across the platform, shaping the future of commodity trading. You will mentor teams, set the AI roadmap, and partner with product and business leaders to deliver impactful capabilities.

• 28 days of annual leave
• Hybrid & workation flexibility
• 2,000 annual L&D budget
• Level Up days for growth
• Fully covered gym memberships
• Paid transportation for office days

• Lead Data Science & Predictive ML initiatives for market intelligence, including modeling, anomaly detection, and optimization
• Oversee transition to centralized Data Science delivering analytics across the ecosystem
• Maintain rigorous model validation, feature engineering, and experimental design
• Drive AI & LLM adoption, building production-ready RAG systems, agentic workflows, and NLP solutions for unstructured data
• Advance AI productization beyond wrappers to deeply integrated features with scalable evaluation and cost management
• Foster rapid prototyping and stay at the cutting edge of AI research
• Consolidate the team from Data Engineering, ML Engineering, and Data Science into a unified, high-velocity group
• Develop and deploy LLM-powered features, including domain knowledge RAG and automated trade summarization
• Provide hands-on technical mentorship on vector databases, prompt evaluation, and MLOps/CI-CD for ML
• Translate complex trading requirements into technical AI solutions in collaboration with Product and Business leaders

• Proven experience in ML and modern LLM development (OpenAI/Anthropic APIs, open-source models, vector stores, and agentic workflows)
• Technical foundation spanning data warehousing to model monitoring
• Strong interest in the trading domain and energy markets
• Experience building and leading a new department with cross-functional teams
• Ability to define culture, tech stack, and workflows for a brand-new function

• Strategic leadership and team-building
• Cross-functional collaboration with Product and Business
• Mentorship and hands-on technical guidance
• ML engineering and data science leadership
• LLM development and deployment (RAG, NLP, agentic workflows)
• Vector databases and prompt engineering/evaluation