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.
You will take ownership of projects across these areas, based on your strengths:
Real-time and batch data processing pipelines at scale
Shared platform infrastructure — feature computation, entity resolution, state management
Graph and network analysis systems
AI agent workflows for operational automation
Across all of these, you will own features end-to-end and have direct input on architecture decisions.
Engineering
Strong programming fundamentals with proficiency in at least one backend language used in production systems
Experience building data-intensive systems at scale — streaming, batch, or distributed computing in production
Solid understanding of system design: data flow architecture, state management, API design, performance trade-offs
Clean, testable, maintainable code. You optimise for simplicity and correctness.
Product & Problem Solving
You consider user impact when making technical decisions
You can identify high-leverage work that reduces operational burden
Comfortable pushing back on unnecessary complexity
AI-Native Development
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.
Skilled at agentic workflows — decomposing complex problems into AI-tractable sub-tasks, orchestrating multi-step execution, and validating outputs with engineering rigor
Strong AI code review: you can evaluate AI-generated code quickly, knowing when to accept, iterate, or discard
Practices verification-driven development — specifications and tests first, AI-generated implementation second, human-validated correctness always
AI / ML (good to have)
Comfortable working with ML/AI systems in production — model serving, anomaly detection, or graph-based analytics
Experience with LLM agent frameworks (LangChain, LangGraph, or similar) is a plus
Domain & Stack (good to have)
Apache Flink or Apache Spark
ScyllaDB / Cassandra, Redis, Elasticsearch, Kafka
Graph databases or graph algorithm implementation
Financial services or regulatory technology domain experience
Small, high-ownership team — you will own significant system components from your first month
AI-native development culture: Claude Code, agentic workflows, and AI-assisted code review are how we work daily
Direct access to product and business context — engineers participate in customer problem discussions, not just ticket execution
Distributed team across Singapore and India
We offer competitive compensation aligned with the calibre of talent we are looking for
