Senior AI Engineer (f/m/d)

Are you ready to be part of the change? Come join us! ⭐

Machine Learning · Generative AI · Agentic Systems

We are seeking a highly skilled and motivated Senior AI Engineer to join our team. The ideal candidate combines strong software engineering fundamentals with hands-on experience building and deploying AI systems in real production environments - whether that background comes from machine learning, data science, or backend/platform engineering that grew into AI.

This role is for builders. You will design and ship end-to-end AI solutions - from data and retrieval pipelines through model and agent orchestration to the interfaces real users touch. We value people who take ownership of a problem, communicate clearly across technical and business audiences, and thrive in a collaborative, international team.

Your mission will be...

Solution Development: Design, develop, and deploy end-to-end AI solutions - RAG and retrieval pipelines, LLM-based applications, agentic workflows, and classical ML where it's the better tool - to improve Siemens processes and products.
Engineering Excellence: Bring production-grade practices to AI: clean code, testing, CI/CD, containerization, observability, and MLOps/LLMOps for evaluation, monitoring, and safe iteration.
Project Ownership: Lead initiatives from concept to deployment, translating business requirements into scalable technical solutions and ensuring timely delivery aligned with business objectives.
Rapid Delivery: Work in a lean, MVP-driven way - prototype fast, validate with real users, then harden what proves valuable.
Collaboration: Work closely with an international team of diverse backgrounds - engineers, product managers, domain experts, and business stakeholders - to integrate AI solutions into existing systems and platforms.
Communication: Clearly articulate complex technical concepts to non-technical stakeholders. Proactively raise concerns, suggest alternatives, and engage in constructive debate when appropriate.
Mentorship & Knowledge Sharing: Mentor colleagues, share reusable patterns and reference architectures, and contribute to the overall skill development of the team.

We are looking for someone with...

Degree in Computer Science, Data Science, Machine Learning, Mathematics, Statistics, Physics, Engineering, or a related field - or equivalent practical experience. A strong track record of shipped systems counts as much as a formal qualification.
5+ years in software engineering, machine learning engineering, or data science roles, including meaningful hands-on experience with modern AI/LLM technologies.
Proven ability to take solutions from exploration to production - not just prototypes and notebooks.
Demonstrated ability to manage and deliver complex projects with multiple stakeholders.
Strong programming skills in Python; additional strength in TypeScript/Node.js, C#, Java, or Go is a plus, especially for building services and user-facing applications.
Practical experience with LLM-based systems: prompt design, RAG architectures, vector search and embeddings, structured output, and evaluation.
Experience with at least one agentic or LLM framework (e.g. LangChain/LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Agent Framework) and an understanding of what it takes to make agents reliable rather than merely impressive.
Familiarity with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) and classical ML/NLP fundamentals.
Experience with cloud-native development (Azure preferred; AWS or GCP equally welcome), including containerization (Docker/Kubernetes) and CI/CD.
Experience designing and working with APIs, data stores, and integration layers (SQL and NoSQL; e.g. PostgreSQL, MongoDB, Azure AI Search, or comparable).
Exposure to MLOps/LLMOps practices: versioning, deployment pipelines, tracing, evaluation, and monitoring of AI systems in production.
Version control and collaborative engineering practices (GitHub/GitLab).
Nice to have: big data and analytics platforms (Snowflake, Spark, Databricks), data visualization, enterprise platforms (Microsoft 365 / SharePoint, SAP, Salesforce), or a research background with publications.
Strong verbal and written communication:
Ability to present ideas concisely and directly in international business contexts.
Comfort proactively raising concerns, suggesting alternatives, and engaging in constructive debate.
Experience bridging communication between global teams and navigating cultural differences in professional settings.
Curiosity about the business domain, not just the technology - the best AI solutions come from engineers who understand the problem deeply.
Independent working style with a proactive approach to problem-solving.
High level of ownership and accountability for project outcomes.
Excellent teamwork and collaboration abilities.
Effective project management and organizational skills.
Fluent English required. German or Portuguese is beneficial but not mandatory.