New opportunity

Senior AI Engineer

Mastercard · Singapore

About this role

As a Senior AI Engineer within Mastercard's AI Foundations team, you will be responsible for building the platforms, services, and data infrastructure that enable AI at enterprise scale. You will design and implement resilient data pipelines, distributed processing frameworks, and production-grade software services to support the full AI lifecycle, from data ingestion and processing to model deployment, evaluation, monitoring, and governance. You will collaborate with data scientists, product teams, and platform engineers to transform research and prototypes into secure, scalable, and maintainable production systems. Responsibilities: Design, build, and operate production-grade AI and ML services, APIs, and platform components. Own end-to-end AI engineering workflows including data preparation, model integration, evaluation, and monitoring. Support cloud migration efforts, transitioning on-premises workflows to platforms like Databricks, Cloudera, or AWS. Develop CI/CD pipelines to streamline deployments and ensure automated, stable workflows. Ensure data quality, security, and compliance with industry standards. Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Advanced proficiency in Python and SQL with strong software engineering fundamentals. High proficiency in Big Data tools including Spark, Hadoop, Hive, Impala, Airflow, and NiFi. Experience in building and deploying production-level data-driven applications and machine learning systems at scale. Preferred Qualifications: Experience in the financial or payments industry. Experience developing integrated cloud applications with Azure, Databricks, AWS, or GCP. Experience managing or working in Agile teams. Experience developing and configuring dashboards.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLSparkHiveImpalaAirflowNiFiHadoopJavaScalaMachine LearningPredictive AnalyticsData EngineeringCI/CDCloud-native servicesDatabricksClouderaAWSAzureGCP

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Production-Grade AI Infrastructure
The role requires building and operating resilient, scalable AI services and data pipelines. You must demonstrate the ability to move beyond prototypes to maintainable production systems, focusing on modular design, API development, and system reliability.
End-to-End MLOps Lifecycle
You are responsible for the full AI lifecycle, including data ingestion, feature engineering, model integration, deployment, and monitoring. Success depends on creating automated, stable workflows using CI/CD practices to ensure consistent model performance.
Cloud Migration and Modernization
A core priority is transitioning on-premises data workflows to cloud-based platforms like Databricks, AWS, or Azure. You must be prepared to re-architect existing processes to leverage cloud-native capabilities while maintaining data security and compliance.
Cross-Functional Collaboration
You will act as a bridge between data scientists, product teams, and platform engineers. You must be able to translate research requirements into technical specifications and align data solutions with broader business objectives.

Likely challenges to think through

Balancing Innovation with Financial Compliance
Operating in the payments industry requires strict adherence to security and governance standards. The challenge is to implement cutting-edge AI solutions without compromising data integrity or regulatory compliance; prepare by discussing how you integrate security-by-design into your development process.
Managing Large-Scale Data Complexity
Working with massive transactional datasets requires high proficiency in distributed processing frameworks like Spark and Hadoop. You may face performance bottlenecks; prepare by highlighting your experience in optimizing complex SQL queries and tuning big data pipelines for efficiency.
Legacy System Integration
The requirement to support cloud migration suggests a complex environment with legacy on-premises systems. You will likely face technical debt or integration hurdles; prepare by sharing strategies for phased migrations and maintaining system stability during transitions.

Practice your answers

Technical practice

Designing Resilient Data Pipelines

What to coverExplain your approach to building fault-tolerant data pipelines using tools like Airflow or NiFi. Focus on how you handle retries, data quality checks, and monitoring to ensure reliability in production environments.

Scaling Machine Learning Workflows

What to coverDescribe how you optimize Spark jobs for large-scale data processing. Discuss strategies for partitioning, memory management, and handling data skew when working with massive transactional datasets.

CI/CD for AI and ML Systems

What to coverDetail the components of a robust CI/CD pipeline for machine learning. Address how you automate testing, model validation, and deployment while ensuring reproducibility and version control for both code and models.

Cloud Migration Strategies

What to coverDiscuss the challenges of transitioning on-premises Hadoop/Hive workflows to cloud-native platforms like Databricks or AWS. Suggest how you would manage data security, cost optimization, and performance parity during such a migration.

Production-Grade API Development

What to coverExplain your process for wrapping ML models into production-ready APIs. Focus on modular design, latency considerations, and how you ensure the service is maintainable and secure for enterprise use.

Monitoring and Observability in AI

What to coverDescribe how you implement monitoring for deployed models to detect data drift or performance degradation. Suggest specific metrics or tools you would use to maintain system reliability and model accuracy over time.

Feature Engineering at Scale

What to coverExplain how you collaborate with data scientists to implement feature engineering pipelines that are both performant and reusable. Discuss the trade-offs between batch processing and real-time feature generation.

Data Governance and Security

What to coverGiven the financial context, explain how you ensure data quality and compliance throughout the AI lifecycle. Discuss your approach to implementing access controls and auditing within data processing frameworks.

Behavioural practice

Cross-functional Collaboration

What to coverDescribe a situation where you had to align technical requirements with non-technical stakeholders or product teams. Explain how you communicated complex AI engineering constraints to ensure the project met business objectives.

Handling Technical Ambiguity

What to coverProvide an example of a time you were tasked with building a system where the requirements were initially unclear. Explain the steps you took to define the scope, iterate on prototypes, and deliver a scalable solution.

Managing Production Incidents

What to coverShare an experience where a production-grade data pipeline or service failed. Detail your immediate actions to restore service, your root cause analysis process, and the long-term changes you implemented to prevent recurrence.

Adapting to New Technologies

What to coverDescribe a time you had to quickly learn and implement a new cloud service or framework to solve a specific engineering problem. Highlight your self-directed learning process and how you validated the tool's suitability for the task.

Sharpen your knowledge

Topics to prepare

MLOps Lifecycle Management
The role requires managing the end-to-end AI lifecycle, including ingestion, training, deployment, and monitoring. Review best practices for model versioning, automated retraining triggers, and drift detection to ensure production-grade reliability.
Big Data Architecture
Mastercard emphasizes large-scale data processing using Hadoop, Hive, and Impala. Study the architectural differences between these tools and how they integrate into distributed processing frameworks like Spark to support high-volume transactional data.
Cloud Migration Strategies
The position involves transitioning on-premises workflows to cloud platforms like AWS or Databricks. Research common challenges in hybrid cloud environments, such as data security, latency, and cost optimization during migration.

Skills to revise

Distributed Data Processing with Spark
Practice writing optimized PySpark jobs for large datasets. Focus on performance tuning, such as managing partitions, avoiding data skew, and efficient memory usage, which are critical for enterprise-scale AI platforms.
Workflow Orchestration with Airflow
Develop and test complex Directed Acyclic Graphs (DAGs) in Airflow to automate data pipelines. Ensure you can handle task dependencies, retries, and monitoring to maintain stable, production-ready workflows.
CI/CD for Machine Learning
Build a sample CI/CD pipeline that automates the testing and deployment of a machine learning model. Focus on integrating automated unit tests for data quality and model evaluation metrics to ensure consistent deployment standards.

Your preparation checklist

Prepare a technical case study on MLOps lifecycle
Draft a detailed walkthrough of a production-grade ML system you have built. Focus on the end-to-end workflow, specifically highlighting how you handled data ingestion, feature engineering, model deployment, and monitoring at scale.
Review Big Data and Cloud migration experience
Prepare specific examples of how you have utilized Spark, Hive, or Airflow to manage large-scale data pipelines. If you have experience migrating on-premises workflows to cloud platforms like AWS, Azure, or Databricks, be ready to discuss the technical challenges and performance optimizations involved.
Demonstrate software engineering fundamentals
Be prepared to discuss your approach to writing maintainable, production-grade code in Python. Focus on modular design, API development, unit testing, and how you implement CI/CD pipelines to ensure stable and automated deployments.
Formulate questions regarding Mastercard's AI infrastructure
Prepare thoughtful questions about the current AI Foundations team's tech stack, their approach to data governance and security in a financial context, and how they balance research-driven prototyping with the need for scalable production systems.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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