Associate AI Developer/ AI Developer (Full stack) - SAP Labs East Asia Singapore

What matters most

• Scalable Data Pipeline Engineering

  The role requires building and maintaining robust, reusable data processing pipelines capable of handling multi-terabyte datasets. You must demonstrate proficiency in Big Data technologies like Hadoop, Spark, and Kafka to ensure system scalability and performance in cloud-based environments.

• Full-Stack AI Integration

  You will bridge the gap between AI research and production-ready software. This involves implementing cloud applications using frameworks like Spring or Flask, while ensuring the underlying system architecture adheres to solid design patterns and object-oriented programming principles.

• Collaborative Global Development

  Success depends on working effectively within a global, cross-functional team of scientists and engineers. You must be able to translate complex business requirements into technical solutions while maintaining clear communication across different time zones and adhering to Agile/Scrum methodologies.

Likely challenges to think through

• Balancing Research and Production Constraints

  A significant challenge is translating experimental AI models into stable, scalable production systems. Prepare by demonstrating how you evaluate the trade-offs between model performance and system latency, and how you apply rigorous quality assurance to ensure reliability in enterprise-grade applications.

• Rapid Technical Immersion

  The role demands the ability to quickly master new technologies and domain-specific terminology. Prepare by showcasing instances where you have rapidly upskilled in a new framework or cloud platform to meet project deadlines, emphasizing your foundational knowledge of computer science principles that allow for quick adaptation.

Topics to prepare

• Scalable Data Pipeline Architecture

  The role requires managing multi-terabyte datasets and building reusable data pipelines. Exercise: Design a conceptual architecture for a data pipeline that ingests, processes, and stores high-volume streaming data using Kafka and Spark. Focus on how you would handle data partitioning and fault tolerance to ensure system scalability.

• Cloud-Native AI Application Design

  The position emphasizes AI-focused cloud applications. Exercise: Review the principles of 12-factor apps and explain how you would apply them to a machine learning service deployed on Kubernetes. Focus on configuration management, backing services, and disposability.

Skills to revise

• Container Orchestration with Kubernetes

  Fundamental knowledge of Docker and Kubernetes is required. Exercise: Create a Dockerfile for a simple Python or Java application and write a basic Kubernetes deployment manifest that includes a service and a horizontal pod autoscaler to demonstrate understanding of container lifecycle management.

• Big Data Framework Proficiency

  The role requires exposure to Hadoop, Spark, and Kafka. Exercise: Write a small Spark job (in Scala or Python) that performs a transformation on a sample dataset, such as filtering or aggregating logs, to demonstrate your ability to handle distributed data processing tasks.

• Design Patterns and Object-Oriented Programming

  Strong competencies in design patterns are mandatory. Exercise: Select two common design patterns (e.g., Singleton, Factory, or Strategy) and implement them in your preferred language (Java, Python, or Go) to solve a common software engineering problem, such as managing database connections or handling different data input formats.

• Review Big Data and Cloud Pipeline Fundamentals

  Prepare to discuss your experience or theoretical knowledge regarding handling multi-terabyte datasets. Focus on how you would design scalable data pipelines using technologies like Spark, Hadoop, or Kafka, and be ready to explain the trade-offs between different cloud-based data processing architectures.

• Demonstrate Proficiency in Two Core Languages

  Select two languages from the required list (Java, Scala, Go, Python, node.js, or C++) and prepare code samples or project walkthroughs that highlight your use of object-oriented programming, design patterns, and multi-threaded programming principles.

• Prepare Containerization and Orchestration Examples

  Be ready to explain how you have used or would use Docker and Kubernetes to ensure application scalability and automation. If you have experience with cloud platforms like AWS, Azure, or GCP, prepare a brief summary of how you deployed a containerized application within those environments.

• Formulate Questions for the Engineering Team

  Prepare insightful questions about the current AI projects at SAP Labs Singapore, such as: How does the team balance research-driven AI experimentation with the need for production-grade system scalability? What are the primary challenges in integrating AI models into existing SAP enterprise business processes?