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# AI Engineer

- **Company:** Siemens Energy
- **Location:** Erlangen, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Software Applications, ARM Architecture, Microsoft Azure, Cloud Computing, Encodings, Computer Programming, Database Applications, Python (Programming Language), Machine Learning, Tensorflow, Search Technologies, Software Engineering, Software Systems, SQL Databases, Data Streaming, Data Processing, Pytorch, Large Language Models, Snowflake, Generative AI, Information Technology, Deployment Automation, HuggingFace, Data Analytics, Data Management, Machine Learning Operations, Data Pipelines, Automation Anywhere, Legacy Systems
- **Published:** August 6, 2026
- **Apply:** https://www.careerjet.de/jobad/de89fd77e6d108a89ad69029c2509bd795

## About the Role

* University degree in Computer Science, Business Informatics, Data Science, AI Engineering, Software Engineering with professional experience in these fields within cloud-based enterprise environments
 * Strong programming expertise in Python and SQL, including the development of reliable, maintainable, and production-ready software solutions and data-driven applications
 * Practical experience with modern AI and machine learning ecosystems, including frameworks and technologies such as PyTorch, TensorFlow, Hugging Face, LangChain, Azure OpenAI, and comparable platforms
 * Expertise in the design and integration of AI-powered solutions into productive business processes, including retrieval-augmented generation (RAG), embedding technologies, vector search, content safety concepts, and cost-efficient AI implementation
 * Solid knowledge of cloud and data platforms, preferably Microsoft Azure or AWS, as well as experience with vector databases and search technologies such as Azure AI Search or Snowflake Cortex
 * Strong business and stakeholder orientation, including the translation of business requirements into user-centric AI solutions, excellent communication and interpersonal skills, business fluency in German and English, a proactive working style, and willingness to travel internationally

## Description

As an AI Engineer (f/m/d), you will become a valued member of a passionate and highly skilled team. Within our Process Digitalization Department, you will serve as the link between software development, data science, and business functions by integrating trained machine learning models and AI capabilities, including Large Language Models (LLMs), into real-world, scalable, and production-ready software applications.
You will collaborate closely with more than 30 factories across our global manufacturing network, translating business requirements into robust, secure, and future-proof AI-driven software solutions. Utilizing technologies and platforms such as Microsoft Azure, Snowflake, Azure OpenAI, Azure AI Search, and Snowflake Cortex, you will design and implement end-to-end AI workflows, retrieval-augmented generation (RAG) solutions, data pipelines, and model integrations that can be deployed, operated, and scaled across the organization.
Your work will play a key role in modernizing legacy systems, automating data flows, enhancing decision-making processes, and establishing a reliable foundation for data-driven, AI-supported, and sustainable business growth.
How You'll Make an Impact

 * Driving AI-enabled modernization initiatives across an international manufacturing network through the application of the company's digitalization methodology, covering core business domains such as production, sales, finance, quality, and procurement
 * Integration of AI and machine learning capabilities into existing IT systems, applications, and user interfaces, ensuring the generation of measurable business value within operational environments
 * Design and implementation of scalable machine learning pipelines, AI workflows, and retrieval-augmented generation (RAG) solutions based on modern cloud, data, and AI technologies
 * Development of robust architectures for data processing, prompt orchestration, embedding-based search, and AI-powered query chains
 * Continuous evaluation of AI-generated outputs with regard to quality, usability, reliability, and hallucination risk, including the derivation and implementation of measurable improvement measures
 * Ensuring the scalability, maintainability, and production readiness of AI solutions through the application of MLOps principles, automated deployment processes, and established operational standards


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