Senior AI Engineer, Data Analytics

Airwallex6 days ago

Location

SG - Singapore

Type

Full Time

Salary

SGD 180,000 – 280,000

Level

Senior

Role

Senior AI Engineer

Posted

Jul 3, 2026

Full TimeSenior

The role

Summary

Senior AI Engineer in Data Analytics at Airwallex leading the design and deployment of agentic AI systems that power customer-facing analytics products. This role combines deep AI engineering expertise with strong data engineering foundations, requiring production experience building AI agents, data pipelines, and data models to answer complex business questions with reliability and accuracy in a fintech environment.

What you'll do

Design and Deploy AI Agent Systems: Architect, build, and ship production-grade agentic AI applications and agent harnesses that reliably answer business questions over governed data. Focus on creating scalable systems that translate complex business requirements into technical implementations with measurable reliability standards.

Develop Core Agent Capabilities: Engineer foundational agent capabilities including retrieval-augmented generation (RAG), natural-language-to-query translation, and dynamic tool or function calling mechanisms. Ensure these capabilities integrate seamlessly with downstream data systems and maintain consistency across the platform.

Enhance AI Output Quality and Reliability: Improve AI system accuracy and robustness through systematic prompt engineering, advanced context engineering, comprehensive evaluation frameworks, output guardrails, and continuous quality measurement. Establish and track metrics that demonstrate model performance improvement over time.

Build Governed Data Foundations: Collaborate cross-functionally to design and maintain governed metrics, semantic data models, and reliable data pipelines that enable agents to reason over consistent, accurate, and secure business data. Ensure data quality standards meet financial services compliance requirements.

Implement Security and Data Governance: Apply enterprise-grade data governance and security practices including sensitive data handling, access control policies, and data isolation mechanisms. Ensure all systems comply with financial services regulatory standards and best practices for multi-tenant data architecture.

Drive Technical Excellence and Leadership: Contribute strong technical judgment to architectural decisions and code reviews, establish high engineering standards across the AI team, and mentor teammates in AI systems thinking. Elevate the technical bar through knowledge sharing, best practices documentation, and collaborative problem-solving.

What we look for

Technical

Production AI Systems DevelopmentHands-on expertise building, training, and deploying agentic AI applications in production environments. Proven ability to move AI systems from proof-of-concept to reliable production services with monitoring, reliability, and performance optimization.

AI Quality and EvaluationDeep experience improving AI system accuracy and reliability through multiple methodologies: evaluation frameworks, output guardrails, quality measurement, prompt optimization, and systematic testing. Familiarity with techniques like few-shot learning, chain-of-thought prompting, and RAG optimization.

Data Engineering FundamentalsSolid foundation in data modeling, data pipeline architecture, and data quality principles. Understanding of how to design data systems that support reliable AI inference, including schema design, data lineage tracking, and metadata management.

Python and SQL ProficiencyStrong Python programming capabilities for building AI systems, data processing, and backend services. Advanced SQL expertise for complex data queries, aggregations, and optimization. Comfortable with both development and data analysis workflows.

Software Engineering PracticesDemonstrated mastery of core software engineering principles: version control, testing strategies, code review processes, system design, and deployment automation. Ability to ship reliable, maintainable code in collaborative team environments.

Education

Bachelor's Degree in Technical FieldBachelor's degree in Computer Science, Mathematics, Finance, Engineering, or closely related field. Educational foundation should provide strong theoretical understanding of algorithms, data structures, and mathematical foundations relevant to AI and data systems.

Experience

5+ Years Production Software DevelopmentMinimum 5 years of professional experience building production software systems, including AI applications, data pipelines, and data models. Experience should demonstrate progression from individual contributor to ownership of meaningful technical problems end-to-end.

Shipping Reliable AI and Data ProductsProven track record of successfully delivering AI and data products to production that are used by internal or external customers. Demonstrated ability to translate ambiguous business requirements and product needs into clear technical solutions with measurable impact.

Cross-Functional CollaborationExperience working across product, data science, and infrastructure teams to align on technical requirements, data models, and system architecture. Ability to influence and make decisions with incomplete information while balancing multiple stakeholder needs.

Skills

Required skills

LLM and Agentic AIHands-on experience with modern large language models, retrieval-augmented generation frameworks, agent orchestration patterns, and prompt engineering. Understanding of LLM capabilities, limitations, and strategies for reliability at scale.

PythonAdvanced Python development for building AI systems, data processing pipelines, and backend services. Proficiency with relevant libraries and frameworks for machine learning and data engineering workflows.

SQL and Data ModelingStrong SQL query development and optimization. Data modeling expertise including schema design, dimensional modeling, and data warehouse architecture. Ability to design data structures that support AI inference patterns.

Data Pipeline ArchitectureExperience designing and implementing reliable data pipelines that extract, transform, and load data for AI systems. Understanding of batch processing, streaming concepts, and data quality validation techniques.

AI System Evaluation and TestingExpertise in evaluating AI system performance through metrics, benchmarking, and quality measurement. Understanding of evaluation frameworks, test design for language models, and guardrail implementation strategies.

Software Engineering FundamentalsMastery of core software engineering practices including version control, code review, testing strategies, system design principles, and deployment automation in collaborative environments.

Nice to have

Semantic and Metrics Layer ArchitectureExperience defining, maintaining, and documenting semantic layers or metrics layers that provide a unified business logic foundation. Knowledge of tools and patterns for creating consistent data definitions across organizations.

Data Governance and SecurityPractical experience implementing data governance frameworks, metadata management systems, and enterprise data security practices. Familiarity with access control, data lineage tracking, and compliance requirements in regulated environments.

Modern Cloud Data PlatformsHands-on experience with cloud-native data platforms such as Databricks, and experience with transformation frameworks like dbt and Apache Spark. Understanding of cloud-native architecture patterns for scalable data systems.

Streaming and Event-Driven DataExperience building high-throughput streaming data pipelines using technologies like Apache Kafka, Apache Flink, or change-data-capture tools. Understanding of real-time data architecture patterns and state management.

Multi-Tenant Product DevelopmentExperience building secure customer-facing or multi-tenant SaaS products with enterprise-grade access control, data isolation, and security architecture. Understanding of tenant isolation strategies and compliance considerations.

Analytics InfrastructureExperience with specialized analytics technologies including low-latency query engines like ClickHouse or similar systems. Knowledge of columnar storage, query optimization, and real-time analytics architectures.

Fintech Industry KnowledgePrior experience in fintech, payments platforms, or other financial services environments. Familiarity with compliance requirements, transaction processing, regulatory frameworks, and financial data semantics.

Compensation & benefits

Salary

SGD 180,000 – 280,000 (annual)

Stock options

Available

Benefits

Competitive Compensation and Equity

Competitive salary reflecting senior AI engineer market rates in Singapore, complemented by equity participation in a unicorn-backed fintech company valued at USD 8 billion with clear growth potential.

World-Class Technical Team and Culture

Work alongside exceptional engineers and AI specialists across 26 global offices. Immerse yourself in a builder culture that values technical excellence, rapid iteration, and ownership mentality across all organizational levels.

High-Impact Project Ownership

Lead meaningful, customer-facing technical initiatives end-to-end with significant business impact. Own complex, high-visibility AI engineering problems that directly influence product direction and business outcomes.

Accelerated Learning and Growth

Accelerate your technical growth through exposure to cutting-edge AI technologies, fintech domain expertise, and cross-functional collaboration. Mentor junior engineers while learning from world-class architects and thought leaders.

Strategic Role in AI Platform Development

Shape the future of AI-powered analytics and financial technology. Participate in building foundational AI infrastructure that scales to hundreds of thousands of global businesses using Airwallex's platform.

Founder-Led Organization with Clear Mission

Join a company with founder energy and clear operating principles focused on democratizing global financial infrastructure. Work toward a mission-driven vision with transparent leadership and strategic alignment.

Interview process

1
Initial Phone Screening — Preliminary conversation with talent acquisition team to assess background, experience with production AI systems, and alignment with role requirements. Expected duration: 20-30 minutes.
2
Technical Phone Interview — In-depth discussion with a Senior AI Engineer or technical lead covering AI system design, data engineering approaches, and specific technical decisions. Expect questions on agentic AI patterns, data modeling, and production considerations.
3
AI Systems Design Assessment — Technical evaluation where you'll design an AI system or data pipeline for a realistic business scenario. This may involve whiteboarding, code discussion, or written design documentation demonstrating your approach to reliability, scalability, and governance.
4
Data and SQL Technical Round — Focused technical interview covering SQL optimization, data modeling fundamentals, and data pipeline design. Expect realistic scenarios involving complex queries, schema design decisions, and data quality considerations.
5
Leadership and Culture Fit Interview — Conversation with an engineering manager or senior leader focused on your approach to mentorship, technical decision-making, collaboration across teams, and alignment with Airwallex's operating principles and builder culture.
6
Executive or Founder-Level Conversation — Final-stage discussion with engineering leadership to explore your vision for AI-powered analytics, your growth trajectory, and mutual cultural and strategic alignment with Airwallex's mission in global fintech.