Software Engineer — Financial Crime Prevention

About the Role

Tookitaki builds FinCense, a financial crime detection platform used by banks and payment providers across Asia-Pacific. The platform combines real-time fraud prevention with anti-money laundering (AML) transaction monitoring, processing millions of transactions daily.

We are looking for an engineer to design and build core platform capabilities across real-time scoring, shared data infrastructure, network analysis, and AI-assisted investigation. You will work on detecting complex financial crime patterns across large-scale transaction networks, where adversaries actively adapt to evade detection.

This is a high-ownership engineering role with direct product influence. The work spans real-time systems, data infrastructure, network analysis, and AI-assisted workflows — you will focus on the areas that match your strengths.

Projects You'll Own

You will take ownership of projects across these areas, based on your strengths:

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  Real-time and batch data processing pipelines at scale

  
  


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  Shared platform infrastructure — feature computation, entity resolution, state management

  
  


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  Graph and network analysis systems

  
  


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  AI agent workflows for operational automation

  
  

Across all of these, you will own features end-to-end and have direct input on architecture decisions.

Requirements

Engineering 

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  Strong programming fundamentals with proficiency in at least one backend language used in production systems

  
  


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  Experience building data-intensive systems at scale — streaming, batch, or distributed computing in production

  
  


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  Solid understanding of system design: data flow architecture, state management, API design, performance trade-offs

  
  


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  Clean, testable, maintainable code. You optimise for simplicity and correctness.

  
  

Product & Problem Solving 

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  You consider user impact when making technical decisions

  
  


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  You can identify high-leverage work that reduces operational burden

  
  


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  Comfortable pushing back on unnecessary complexity

  
  

AI-Native Development

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  Our team operates in an AI-native development environment where coding agents (Claude Code, Cursor, or similar) are heavily integrated into daily workflows. You should be comfortable working this way.

  
  


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  Skilled at agentic workflows — decomposing complex problems into AI-tractable sub-tasks, orchestrating multi-step execution, and validating outputs with engineering rigor

  
  


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  Strong AI code review: you can evaluate AI-generated code quickly, knowing when to accept, iterate, or discard

  
  


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  Practices verification-driven development — specifications and tests first, AI-generated implementation second, human-validated correctness always

  
  

AI / ML (good to have)

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  Comfortable working with ML/AI systems in production — model serving, anomaly detection, or graph-based analytics

  
  


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  Experience with LLM agent frameworks (LangChain, LangGraph, or similar) is a plus

  
  

Domain & Stack (good to have)

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  Apache Flink or Apache Spark

  
  


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  ScyllaDB / Cassandra, Redis, Elasticsearch, Kafka

  
  


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  Graph databases or graph algorithm implementation

  
  


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  Financial services or regulatory technology domain experience

  
  

Working Environment

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  Small, high-ownership team — you will own significant system components from your first month

  
  


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  AI-native development culture: Claude Code, agentic workflows, and AI-assisted code review are how we work daily

  
  


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  Direct access to product and business context — engineers participate in customer problem discussions, not just ticket execution

  
  


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  Distributed team across Singapore and India

  
  


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  We offer competitive compensation aligned with the calibre of talent we are looking for