Artificial Intelligence Engineer

• Position:** Databricks AI / ML Engineer (m/f/d)
• Location:** Vienna, Austria (Hybrid, approx. 50% on-site / 50% remote)
• Contract Type:** Contract, Full-time (100%)
• Start:** March/April 2026
• Duration:** 3 years (optionally extendable up to a maximum of 5 years)
• Daily Rate:** €400-450/day
• Project Language:** English

For a long-term data and AI program, we are seeking a **Databricks AI / ML Engineer (m/f/d)**. The goal is the development, implementation, and operation of scalable **machine learning and LLM solutions** on **Azure Databricks** – from data preparation and feature engineering to MLOps, deployment, and monitoring.

The focus is on **Big Data Engineering**, **ML/LLM workloads**, **MLOps automation**, and seamless integration into the **Microsoft ecosystem**.

Big Data Engineering, **ML/LLM workloads**, **MLOps automation**, and **Microsoft integration**.

Responsibilities

- Data analysis and prototyping with **Python in Azure Databricks** using common ML frameworks

- Development and operation of **Big Data pipelines** with **Apache Spark**, **Delta Lake**, and **Databricks SQL**

- Feature engineering** as well as training, versioning, and deployment of models with **Databricks MLflow**

- Development and operation of **ML and LLM workloads** on Azure Databricks (including **Unity Catalog**, performance and cost optimization)

- End-to-end integration of solutions into the **Microsoft ecosystem** (e.g., API and interface design, orchestration with **Azure Functions** and **Logic Apps**)

- Setup and further development of **MLOps and CI/CD pipelines** for automated training, testing, deployment, and monitoring of ML and LLM models and agents

- Execution of **Model and data monitoring** (model performance, data drift, bias) including maintenance and update processes

- Use of **AutoML tools** to accelerate prototypes and experiments

- Ensuring scalability, security, and stable operational processes of the developed solutions

Technical Requirements

- Solid knowledge of **data analysis and prototyping with Python** in Azure Databricks

- Experience with **machine learning frameworks** such as TensorFlow, PyTorch, and scikit-learn

- Practical experience in **big data engineering** with Apache Spark, Delta Lake, and Databricks SQL

- Expertise in **feature engineering** and model deployment with **managed Databricks MLflow**

- Experience in developing and operating **ML and LLM workloads** on Azure Databricks

- Experience with **end-to-end integrations** in the Microsoft ecosystem

- Experience in building **MLOps and CI/CD pipelines** for ML and LLM models as well as agentic workflows

- Experience in **model and data monitoring** (performance, drift, bias) including appropriate maintenance strategies

- Practical experience using **AutoML tools**

- Availability of **a separate development environment from the client's production system** that meets current standards for data security and access control (including proof of infrastructure)

Advantages

- Experience with **data and AI governance**

- Experience in the design and implementation of **agentic approaches** (agents, multi-agent systems, agentic workflows) with Azure resources

- Experience in implementing **end-to-end Databricks projects** (from data preparation and feature engineering to model training and deployment, MLOps, and monitoring)

- Industry knowledge in the **energy industry**

- Structured and analytical approach to work, high quality awareness, and a strong sense of responsibility

- Excellent communication skills with technical and professional stakeholders

- Teamwork skills and willingness to share knowledge