AI Software Engineer

Siemens Digital Industries Software is a leading provider of solutions for the design, simulation, and manufacture of products across many different industries. Formula 1 cars, skyscrapers, ships, space exploration vehicles, and many of the objects we see in our daily lives are being conceived and manufactured using our Product Lifecycle Management (PLM) software.

Are you passionate about AI and excited about taking Generative AI capabilities from prototype to production inside a real enterprise? We are looking for a dedicated AI Engineer to join the CApS AI Engineering team and help build and productionise embedded AI capabilities across CApS—delivering solutions that are secure, scalable, measurable, and operationally ready.

Role Purpose-

• Embedded AI delivery (copilots, assistants, agentic workflows, retrieval-augmented experiences),
• Platform & tooling foundations (golden paths, reusable templates, reference integrations),
• Governance-by-design (risk controls, auditability, privacy/security expectations),
• Enablement (helping other CApS teams adopt AI patterns confidently and safely).

Role Clarification (Included and Excluded Responsibilities)

• A partner in designing, building, and operating AI-enabled capabilities that solve real operational and product problems.
• An enabler of scale through reusable patterns, templates, and guidance.

This role is not intended to be:

• A helpdesk or on-demand support line for ad hoc experimentation.
• A centralized team that “does AI for everyone” end-to-end.
• A dumping ground for unfinished prototypes.

Key Responsibilities-

·As an AI Engineer, you will design, build, and maintain AI-enabled capabilities powered by Large Language Models (LLMs), working closely with product and platform teams.

You’ll make a difference by:

Designing & delivering embedded AI capabilities (copilots, agents, workflows)

• Designing and implementing AI features using validated patterns such as RAG, tool calling, agents, and multi-step workflows.
• Integrating AI capabilities into existing services and applications via APIs and well-structured backend components.
• Ensuring AI features are scalable, high-performance, and aligned with enterprise expectations.

Productionising AI (from intake to operation)

• Moving AI use cases through a practical lifecycle: intake → risk classification → gated approvals → build → test → deployment → operation.
• Defining operational readiness (runbooks, monitoring, rollout strategies) and supporting production incidents and solving when needed.

Quality, evaluation, and continuous improvement

• Developing and delivering evaluation plans and test harnesses for AI systems (offline tests, regression checks, retrieval scoring, and reliability checks).
• Defining what “good” looks like using clear metrics (e.g., success rate, latency, cost, failure modes) and using those metrics to drive iteration.
• Implementing fallback, recovery, and human-escalation mechanisms for failure scenarios where appropriate.

Observability and measurable operations

• Instrumenting AI services for tracing, logging, cost signals, and feedback loops to improve quality and reliability over time.
• Contributing to operational dashboards and alerting patterns to support debugging and production learning cycles.

Platform & tooling (“golden paths”)

• Contributing to reusable foundations: templates, CI/CD hooks, reference implementations, and integration patterns that make safe AI delivery repeatable.
• Partnering with platform and security collaborators to embed security, access control, and compliance requirements into paved paths.
• Building and integrating cloud-native AI components on major platforms (AWS and Azure), including handled agent/runtime and retrieval services (e.g., Amazon Bedrock AgentCore and Knowledge Bases; Microsoft Foundry and Foundry Tools) where appropriate for the workload and compliance context.

Enablement & adoption across CApS

• Pairing with development teams to onboard them to approved AI patterns; supporting adoption through workshops, office hours, and documentation.
• Helping accelerate the organization’s shift toward AI-assisted products and AI-assisted engineering through practical guidance and shared