Senior AI Developer / Architect - SAP Labs East Asia Singapore

What matters most

• Architectural Design and Documentation

  You must define Architecture Decision Records (ADR) and Architecture Concept Documents (ACD) for new features. This requires translating complex business requirements into scalable, production-grade microservices architecture while ensuring alignment with SAP's standards.

• High-Performance Conversational AI Engineering

  The role centers on building Joule and other conversational services. You are expected to deliver code that meets top-notch performance benchmarks, requiring deep expertise in Java, Spring Boot, and event-driven patterns to handle high-concurrency AI workloads.

• Technical Leadership and Mentorship

  Beyond individual contribution, you must foster a high-performance culture by mentoring junior engineers and conducting rigorous code reviews. You are expected to drive engineering excellence across the full development lifecycle.

• Cloud-Native Integration and Delivery

  You must manage CI/CD pipelines across multiple hyperscalers (AWS, GCP, Azure) using Jenkins. This involves maintaining secure, containerized (Docker/Kubernetes) microservices that integrate seamlessly within the SAP Business Technology Platform ecosystem.

Likely challenges to think through

• Balancing Innovation with Enterprise Stability

  Integrating cutting-edge AI technologies like RAG, LLMs, and Knowledge Graphs into established enterprise software requires balancing rapid experimentation with the strict reliability and security requirements of SAP's production environments.

• Global Cross-Functional Collaboration

  Working within a global team across different time zones while coordinating with scrum teams and stakeholders can create communication friction. Prepare by demonstrating experience in managing distributed technical dependencies and maintaining alignment on project goals.

• Managing Complex Multi-Tenant Environments

  Developing for multi-tenant, cloud-native microservices introduces significant performance and isolation challenges. You should be prepared to discuss strategies for optimizing resource usage and ensuring consistent performance across diverse customer deployments.

Topics to prepare

• Microservices Architecture and Event-Driven Design

  The role requires designing production-grade conversational services. Review patterns for decoupling services, managing state in multi-tenant environments, and implementing event-driven communication using Kafka. Exercise: Draft an Architecture Decision Record (ADR) for a hypothetical conversational AI feature, detailing how you would handle asynchronous event processing between a bot service and a backend data pipeline.

• Retrieval-Augmented Generation (RAG) and AI Agents

  As the role involves building AI products like Joule, understanding the integration of LLMs with external data is critical. Review how RAG architectures combine vector databases with LLMs to ground responses. Exercise: Design a high-level workflow for an AI agent that retrieves context from a Knowledge Graph to answer enterprise-specific queries, focusing on latency and accuracy.

Skills to revise

• Spring Boot and Java Development

  Deep practical experience in Spring Boot is a core requirement for building maintainable microservices. Exercise: Refactor a monolithic service into a modular Spring Boot application, ensuring proper dependency injection, RESTful API design, and unit testing coverage.

• Cloud-Native CI/CD and Container Orchestration

  The role requires managing deployments across hyperscalers using Docker, Kubernetes, and Jenkins. Exercise: Create a Jenkins pipeline script that automates the build, containerization, and deployment of a Java microservice to a Kubernetes cluster, incorporating security scanning steps.

• Performance Engineering and Monitoring

  The role demands solving complex performance challenges in global systems. Exercise: Use JMeter to simulate high-concurrency traffic on a REST API and analyze the results using Dynatrace metrics to identify potential bottlenecks in service response times.

• Prepare a technical case study on microservices architecture

  Draft a summary of a past project where you designed a scalable, event-driven microservices system using Java and Spring Boot. Focus on how you handled multi-tenancy, performance bottlenecks, and integration challenges within a cloud-native environment.

• Develop an architectural design document (ADR/ACD) example

  Create a sample Architecture Decision Record (ADR) or Architecture Concept Document (ACD) for a hypothetical AI-integrated feature. This demonstrates your ability to document technical trade-offs, design choices, and alignment with enterprise standards.

• Review CI/CD and cloud deployment strategies

  Prepare to discuss your experience managing Jenkins pipelines across hyperscalers like AWS, GCP, or Azure. Be ready to explain how you ensure secure software development lifecycles (SDLC) and manage container orchestration using Docker and Kubernetes.

• Synthesize experience with modern AI and LLM patterns

  Prepare to articulate your practical experience or research regarding RAG (Retrieval-Augmented Generation), Knowledge Graphs, and AI Agents. Explain how you would integrate these into conversational services to improve accuracy and performance.

• Formulate questions for the hiring team

  Prepare thoughtful questions regarding the team's current Agile maturity, the specific integration challenges of the Joule conversational service, and how the team balances rapid AI innovation with enterprise-grade stability and security requirements.