AI Inhouse Consultant

Helping our customers to sell more parts & make things easier with our data and solutions based on our globally leading data standards.

We are TecAlliance — founded by more than 30 automotive aftermarket companies as a neutral Data‑as‑a‑Service market connector, serving as the neutral data terrain that interconnects them. With over 1,000 employees worldwide and customers in 140 countries, we enable the market — we never compete with it.

You will join a Data & Machine Learning team of 10+ people that delivers AI solutions end‑to‑end, from business use‑case definition to working, production‑ready prototypes using agentic coding. For example, the team recently collaborated within an AWS & AI Workshop in Frankfurt to prototype multiple TecAlliance use cases within days, pairing engineers with AI agents to build fully working solutions on secure enterprise data. Ship fast, learn faster: we move quickly from idea to working AI prototypes, validate early in real contexts, and scale what works and drop what doesn’t. We have millions of datapoints. But without a business case, they’re just noise. And that’s the job as AI Inhouse Consultant for.

Your role

Turn real TecAlliance business problems into working AI solutions, owning the journey from first use‑case discussion to hands‑on prototyping, validation, and production handover together with internal stakeholders.

Deliver visible, measurable outcomes with AI — whether that’s reducing manual work, improving data or product quality, or enabling new capabilities — and take responsibility for progress against agreed goals.

Work hands‑on with the DSML team to prototype and ship AI use cases fast, for example in collaborative settings where engineers pair with AI agents to build end‑to‑end solutions (UI included) within days, not months.

Build, integrate, and operate ML and LLM‑based components using Python and the modern ML stack, connecting models to TecAlliance data pipelines and products and iterating based on real feedback from Product, Engineering, and Operations.

Make your work reusable and transparent: document solutions, evaluations, and lessons learned; keep code reproducible and production‑ready; and contribute actively to the team’s shared standards and playbooks.

Stay curious and forward‑looking by experimenting with new AI, GenAI, and agentic approaches, sharing insights with the team, and helping scale proven practices so more colleagues can benefit in their daily work.

Your profile

• High learning velocity. Prototype early. Validate fast. Ship reliably and build bridges for the business via infrastructure and through systems: end‑to‑end AI/ML delivery with sound judgment, accelerated via AI assisted systems such as Copilot/Cursor/Claude.
• Deep hands‑on expertise in Python.
• Comfortable with scripting languages especially within WSL.
• Basic cloud and infrastructure awareness (AWS, Azure, or similar).
• Strong academic foundation in Computer Science, Data Science, Machine Learning, Mathematics, Statistics, or a comparable technical discipline; continuous professional development in cloud‑native ML and MLOps practices is an advantage.
• Proficiency in English (B2+), German highly advantageous.

The ideal candidate (m/f/d) will bring

• Experience with Python and the modern ML stack (PyTorch or TensorFlow, scikit‑learn, pandas).
• Working knowledge of at least one major cloud ML platform (Azure ML, AWS SageMaker or GCP Vertex AI) and MLOps tooling (MLflow, Docker, CI/CD).
• Experience with LLMs, retrieval‑augmented generation (RAG) and generative‑AI frameworks (LangChain, LlamaIndex) is a strong plus.