Head of Applied AI (m/f/d)

Founded in 2016 and headquartered in Munich, enmacc is a high growth scale-up with a diverse team of over 100 professionals. We’re passionate about energy, technology, and building something that makes a real impact. If you are looking for an entrepreneurial environment where your work directly contributes to a more sustainable and digital energy future, we invite you to submit your application below.

Your mission: As the Head of Applied AI, you will lead enmacc’s transition into an AI-first trading platform. You will unify our distributed data talent into a high-performance subdepartment, spearheading a dual-track strategy: leveraging Classical Data Science for market intelligence and Modern LLM Development for product automation. You are the bridge between raw energy market data and the intelligent features that will define the future of commodity trading.

Responsibilities

1. Data Science Predictive ML Market Intelligence: Drive the development of predictive models, anomaly detection, and optimization algorithms tailored to the energy trading domain. Core DS Leadership: Oversee the transition of Data Science from product-specific silos into a centralized powerhouse that provides advanced analytics across the entire enmacc ecosystem.

Statistical Rigor: Maintain high standards for model validation, feature engineering, and experimental design. 2. Modern AI LLM Adoption Generative Strategy: Lead the adoption of LLMs and Generative AI.

This includes building production-ready RAG (Retrieval-Augmented Generation) systems, agentic workflows, and NLP solutions for unstructured trading data (chats, news, contracts). AI Productization: Move beyond "wrappers" to build deeply integrated AI features that solve complex domain problems, focusing on evaluation frameworks and cost-efficient scaling. Rapid Prototyping: Foster a culture of fast experimentation with the latest AI research to keep enmacc at the cutting edge.

Strategic Leadership: Build and lead a unified Data AI group, integrating existing specialists (Data Engineering, ML Engineering, and Data Science) into a high-velocity team. AI LLM Roadmap: Lead the development and deployment of LLM-powered features. This includes exploring RAG (Retrieval-Augmented Generation) for domain knowledge, automated trade summarization, and intelligent market assistants.

Product Innovation: Drive the ML roadmap for product development, identifying where predictive modeling and NLP can create a competitive advantage in energy and commodity markets. Technical Excellence: Provide hands-on technical mentorship. You should be as comfortable discussing vector databases and prompt evaluation as you are with CI/CD for ML (MLOps).

Requirements

into technical AI solutions. Your Team You will consolidate a currently distributed team to foster architectural consistency and shared AI standards: Data Engineering (from Platform) – Ensuring the data "plumbing" supports real-time AI. ML Engineering (from Platform) – Managing the lifecycle of models from experiment to production.

Data Science (from Product) – Translating business problems into statistical and algorithmic solutions. , working with OpenAI/Anthropic APIs, open-source models, vector stores, and agentic workflows). Technical Foundation: You understand the full stack—from data warehousing to model monitoring.

" Trading Interest: You are genuinely interested in the trading domain . You see the potential for AI to navigate the complexities of energy markets and commodity flows. Strategic Builder: You are excited to define the culture, tech stack, and workflows of a brand-new department from the ground up.

Don't match every single requirement? We still want to hear from you!

Compensation

pack