Note: The job is a remote job and is open to candidates in USA. Siemens develops asset management software that uses connected insights, AI-powered technology, and intelligent asset management to help organizations improve operations and asset performance. The AI Agentic Software Engineer will build, test, document, and support scalable SaaS applications and integrations while using AI-assisted development tools and agentic workflows throughout the software development lifecycle.
Responsibilities
- Build innovative, performant features into our next-generation SaaS applications, applying core computer science principles — data structures, algorithms, and design patterns — to deliver reliable, scalable solutions
- Design, implement, test, and document services and components that scale across multiple projects as common services or shared modules, within scope, cost, time, and quality constraints
- Embed quality throughout the full agile product lifecycle: requirements, design, code, testing, delivery, and production support — including owning root-cause resolution of production defects
- Write clean, maintainable code; uphold and continuously improve engineering standards in a professional environment (source control, CI/CD, shortened release cycles, automated testing)
- Partner with product owners and senior engineers to translate user requirements into technical specifications, offering pragmatic feasibility guidance and data-driven decision making
- Leverage AI-assisted developer tools (e.g., GitHub Copilot, Claude, Cursor, or equivalent) to accelerate coding, code review, test generation, and debugging — and actively share practices and learnings with the team
- Participate in agentic development workflows where AI agents assist with or autonomously handle defined stages of the SDLC — such as story enrichment, scaffolding, test generation, and PR summarization — reviewing agent output critically and ensuring quality before merge
Skills
- Bachelor's degree in Computer Science,Software Engineering, or a related technical discipline, OR 5 years of equivalent professional experience in lieu of degree
- 1–3 years of professional software engineering experience building and maintaining web-based or service-oriented applications
- Solid grounding in data structures, algorithms, object-oriented programming, SQL/databases, REST API design, and SOLID principles
- Experience with unit testing, mocking frameworks, and test automation — with a quality-first mindset across the full development lifecycle
- DevOps mindset: familiarity with CI/CD pipelines, exception handling, logging, monitoring, and operational metrics in a mature SDLC environment
- Strong communication, partnership, and teamwork skills; experience working in agile methodologies (Scrum, Kanban, or equivalent)
- Qualified Applicants must be legally authorized for employment in the United States and will not require employer sponsored work authorization now or in the future for employment in the United States
- Hands-on experience with AI coding assistants and prompt engineering — using tools like GitHub Copilot, Claude, Cursor, or similar to accelerate code generation, test scaffolding, documentation, and code review
- Exposure to agentic development patterns: understanding how LLM-powered agents can be chained or orchestrated to automate multi-step engineering tasks (e.g., requirement analysis code generation test writing• PR creation), and experience evaluating or integrating such pipelines into a team workflow
- Familiarity with LLM tool-use, function calling, or structured output techniques that enable AI agents to interact with APIs, codebases, and external systems in a controlled, verifiable way
- Cloud platform exposure — AWS, Azure, or GCP — including containers (Docker) and orchestration (Kubernetes)
- Experience with event-driven or messaging architectures (Kafka, SQS, RabbitMQ, or similar)
- Open-source contributions, personal projects, hackathon participation, or active involvement in developer communities — especially AI/ML or developer-tooling focused
- Certifications in cloud platforms, AI/ML fundamentals, technology, or agile methodologies
- Prior experience in agile at scale across globally distributed teams
Benefits
- This is a remotely based role in the U.S.
- Opportunity to grow your career
- Continuous learning
Company Overview