Siemens Global Business Services (GBS) enables Siemens units worldwide and external customers to accelerate their business transformation into a sustainable and digital future.

Our portfolio comprises services driven by expertise and the latest technology – with a strong focus on innovation and digitalization in areas like Finances, Business Administration, Human Resources, Procurement, Sales and After-Sales. 

In our GBS Western Europe and Africa Hub we are already more than 1500 connected people, from over 50 different nationalities spread across 14 countries. If you value diversity and innovation, you’ll feel right at home here!

Discover our purpose, culture, and what makes GBS unique! Check out here!

Our MarCom, Sales and Delivery Services (MSD) business line offers expertise in Marketing, Communication, Sales, and Delivery. We deliver services ranging from sales enablement, order-to-delivery processes up to after-sales support. With our end-to-end services we empower sellers, streamline operations, and enable businesses to deliver outstanding customer experiences across all touchpoints.

Are you ready to be part of the change? Come join us!  ⭐

We are seeking a Senior AI Software Engineer who will take end-to-end ownership of complex, production-grade AI solutions. This role goes beyond implementation—it requires strong architectural thinking, technical leadership, and the ability to design scalable, secure, and cloud-native AI systems. You will act as a technical anchor for AI initiatives, guiding design decisions, mentoring engineers, and ensuring long-term sustainability of AI platforms.

Your mission will be…

1. Technical Leadership & Ownership

• Own the design, development, and delivery of mission-critical AI/ML solutions. • Drive technical decisions related to architecture, frameworks, and deployment strategies.

• Define and enforce engineering best practices across AI projects.

• Serve as the go-to technical expert for complex AI-related challenges.

• Lead code reviews, refactoring initiatives, and technical debt reduction.

2. AI/ML Solution Engineering

• Architect and implement end-to-end AI/ML pipelines (data ingestion → feature engineering → training → deployment → monitoring).

• Ensure models are production-ready with proper observability, explainability, and lifecycle management.

• Optimize AI solutions for performance, reliability, and cost efficiency.

• Handle large-scale, low-latency inference systems where applicable.

3. Cloud-Native & Modern Architecture

• Design and implement AI solutions using modern cloud-native principles.

• Build AI services using microservices, event-driven, and API-first architectures.

• Ensure resilience, scalability, and fault tolerance of AI systems.

• Drive adoption of best-fit cloud services (AWS, Azure, GCP).

• Define deployment patterns using containers, orchestration, and IaC.

4. Integration & Enterprise Readiness

• Design robust integration strategies between AI systems and enterprise platforms.

• Ensure AI services are loosely coupled, well-governed, and reusable.

• Implement asynchronous messaging, event streaming, and API gateways where appropriate.

• Ensure compliance with security, data privacy, and regulatory standards.

5. MLOps & Production Excellence

• Define and implement MLOps standards for CI/CD, monitoring, retraining, and rollback.

• Introduce automation across the model lifecycle.

• Establish metrics for model performance, drift detection, and system health. • Drive observability and reliability practices for AI platforms.

6. Mentoring & Cross-Team Collaboration

• Mentor mid-level engineers and uplift overall team capability.

• Collaborate with architects, data scientists, and product owners.

• Translate ambiguous business needs into scalable technical solutions.

• Support pre-sales, estimations, and feasibility discussions when required.