• Building AI-enabled systems • Integrating LLMs and RAG solutions • Working with cloud services, data pipelines, and model-driven features • Collaborating with international teams on technical requirements

Title: AI Engineer Location: Orlando United States Location United States of America Florida Orlando Remote vs. Office Hybrid (Remote/Office) Company Siemens Energy, Inc. Organization Gas Services Business Unit Central Full / Part time Full-time Experience Level Job Description: A Snapshot of Your Day This role is a new addition to a Gas Services Engineering Team, focused on driving innovation through the development of LLM‑powered applications that enhance internal engineering processes. The position spans the full lifecycle of AI solutions, from identifying high‑value use cases and rapid prototyping to full‑stack development, cloud deployment, and long‑term application support. The role is centered on three core areas: AI application strategy and prototyping, full‑stack development and system integration, and application support and lifecycle management. The ideal candidate brings hands‑on experience as a full‑stack engineer with a strong foundation in AI/ML, including proficiency in Python, experience with LLM frameworks, and a solid understanding of the software development lifecycle, cloud platforms, and production support of enterprise applications. How You’ll Make an Impact - 40% Full-Stack Development and Integration - Design, build, and deploy robust, scalable full-stack applications, integrating front-end interfaces with back-end LLM services. - Develop and manage APIs to ensure seamless integration of AI models into existing engineering platforms and workflows. - Implement and manage CI/CD pipelines to automate the testing and deployment of applications. - Collaborate with and guide external contractors on specific development tasks, ensuring alignment with project standards and timelines. - 30% AI Application Strategy and Prototyping - Collaborate with engineering stakeholders to identify and evaluate opportunities for new LLM-based applications that address business needs. - Translate user requirements and process challenges into technical specifications for AI-powered tools and service assistants. - Develop proof-of-concept models and rapid prototypes to validate the feasibility and potential impact of new application ideas. - 30% Application Support and Lifecycle Management - Provide ongoing technical support for deployed AI applications, performing troubleshooting and resolving reported issues. - Monitor application performance, reliability, and user feedback to identify and implement continuous improvements and optimizations. - Create and maintain comprehensive documentation for application architecture, codebases, and operational procedures. What You Bring - Bachelor's degree in a technical field. Mechanical or Aerospace Engineering preferred / not required. - 2-5 years' AI/ML engineering. Candidates with more experience may be considered for a higher-level position. - Developing and deploying applications using Python and modern AI/LLM frameworks. - Large language models (LLM), generative AI, NLP, and machine learning - Frameworks and patterns like LangChain, Model Context Protocol, and agentic workflows - Proficiency in Python, API development, and application design - AWS and/or Azure deployment and support - DevOps practices - Modern front‑end frameworks such as React - Applicants must be legally authorized to work in the US without the need for current or future employer sponsored work authorization. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. About the Team Our Gas Services division offers Low-emission power generation through service and decarbonization. Zero or low emission power generation and all gas turbines under one roof, steam turbines and generators. Decarbonization opportunities through service offerings, modernization, and digitalization of the fleet. Who is Siemens Energy? At Siemens Energy, we are more than just an energy technology company. With ~100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation. Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation. Rewards/Benefits - Career growth and development opportunities - Supportive work culture - Company paid Health and wellness benefits - Paid Time Off and paid holidays - 401K savings plan with company match - Family building benefits - Parental leave

• Design and evolve automation frameworks for public cloud infrastructure deployments at scale. • Enable secure cloud adoption by automating governance, audit, security, and compliance controls. • Use, develop and maintain AI services across AWS Platform (Cloud Native) • Build and maintain infrastructure and application monitoring to detect, prevent, and respond to incidents. • Drive cloud reliability, performance, and cost optimisation through best practices and continuous improvement. • Develop tools and mechanisms that eliminate repetitive operational tasks and improve engineering efficiency. • Review code, assess technical implementations, and provide actionable feedback to improve platform tooling. • Participate in on‑call rotations and serve as an escalation point for cloud service incidents.

• Build and maintain automated evaluation pipelines for AI model quality. • Implement task-specific benchmarks and test suites. • Design quality dashboards tracking accuracy, regression, and safety metrics. • Implement automated regression testing for every model iteration. • Build comparison frameworks for side-by-side evaluation of model variants. • Analyze evaluation results to identify failure modes and report to the ML team. • Maintain evaluation datasets: versioning, quality validation, coverage analysis. • Support A/B testing infrastructure for production model validation.