Senior AI Engineer

• MongoDB
• Google Cloud Platform
• scikit-learn
• Microsoft 365
• TensorFlow
• Azure
• Salesforce
• PyTorch
• SharePoint
• LlamaIndex
• GitLab
• Docker
• SQL
• NoSQL
• LangGraph
• Node.js
• PostgreSQL
• LangChain
• Azure Search
• Semantic Kernel
• Java
• GitHub
• C#
• Kubernetes
• TypeScript
• SAP
• Python
• Apache Spark
• CrewAI, * 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.
• 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.

• 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.
• 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.
• Familiarity with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) and classical ML/NLP fundamentals.
• 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.
• 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., * 5+ years in software engineering, machine learning engineering, or data science roles, including meaningful hands-on experience with modern AI/LLM technologies.
• 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.
• 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).
• Experience bridging communication between global teams and navigating cultural differences in professional settings.

Unser Angebot

• At Siemens we are committed to making real what matters.
• You’ll work on AI that reaches real users inside one of the world’s most innovative technology companies - with access to modern AI tooling, a strong engineering culture, continuous learning opportunities, flexible working models, and an international, diverse team that will both support and challenge you.
• We value equal opportunities and welcome applications from people with diverse backgrounds.