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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - **Company:** Siemens AG - **Location:** Wien, Austria (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Java (Programming Language), Microsoft Windows, Artificial Intelligence, Amazon Web Services, Microsoft Azure, C Sharp (Programming Language), Cloud Computing, Computer Programming, Continuous Integration, Data Stores, Data Visualization, Github, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Node.Js, NoSQL, Tensorflow, Standard Sql, Salesforce.Com, SAP (Applications), Search Technologies, Microsoft SharePoint, Software Engineering, SQL Databases, TypeScript, Management of Software Versions, Google Cloud, Pytorch, Delivery Pipeline, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Backend, Gitlab, Containerization, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Api Design, Software Version Control, Docker, Databricks - **Published:** September 25, 2026 - **Apply:** https://devjobs.at/job/2d5db289dbe4ec0a74cb1ae372062b8f ## About the Role * 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. ## Description * 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. ## Related Videos - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [WeAreDevelopers LIVE - Modern DevOps for IoT Devices and More](https://www.wearedevelopers.com/videos/1805-wearedevelopers-live-modern-devops-for-iot-devices-and-more) - [The Missing Layer Between Enterprise Data and AI Agents](https://www.wearedevelopers.com/videos/100286-the-missing-layer-between-enterprise-data-and-ai-agents) - [Building a Multi-Agent Orchestration Engine That Actually Follows the Rules](https://www.wearedevelopers.com/videos/100159-building-a-multi-agent-orchestration-engine-that-actually-follows-the-rules) - [NoSQL Data Modeling for Front-end Developers](https://www.wearedevelopers.com/videos/297-nosql-data-modeling-for-front-end-developers) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)