Client Introduction
My client is a Southeast Asia–headquartered data & analytics platform powering decision intelligence for enterprises and public-sector programs.
The company combines a proprietary data engine (with custom query capabilities) and LLM-driven analysis to turn complex, multi-source datasets into secure, actionable insight at speed.
The platform operates on a token-based model and is evolving into an AI-native product (not a bolt-on). Flat hierarchy, hands-on leadership, and a hybrid setup (~2-3 days in office).
Job Responsibilities
Reporting to a manager (oversight by CTO), your role involves:
Ship end-to-end features across React (front end) and (back end), working with MongoDB and Redis for performance and reliability.
Build and consume RESTful APIs; write functional/integration tests; and partner with operations to diagnose and fix production issues.
Take ownership of a complex, sensitive production system used in high-stakes contexts - design changes carefully, document thoroughly, and uphold security and reliability standards.
Collaborate with product, design, and engineering to translate ideas into secure, user-centric features on a platform that ingests diverse data for LLM-powered insights.
Contribute to go-to-market work around the proprietary data engine as a base layer for AI applications.
Job Requirements
At least 1 year experience (internships welcome). Strong JavaScript fundamentals (there will be a JS test).
Practical exposure to React, , MongoDB; Redis experience is a plus.
Comfort with REST APIs, Git, and Agile ways of working; ability to convert design mock-ups into working features.
Bonus:
Express, AWS, microservices, Docker/Kubernetes, and monitoring (Grafana/Prometheus/Datadog).
Mindset:
resourceful, curious, accountable, and clear in technical communication.
Why You Should Join Them
AI-native trajectory: Build on a platform where AI/LLMs are core, not an add-on-ideal for engineers who want to work at the intersection of data platforms and applied AI.
Serious engineering, real impact:
Inherit and improve a mission-critical system used in sensitive environments-learn reliability, security, and scale the right way.
Own the data engine:
Work on a proprietary database layer that other products and AI teams can build upon-shape both product and platformisation.
Clear growth paths:
As the system scales, grow into Engineering Lead/Manager, Solutions Architect, or Product Lead (small product team = visible impact and progression).
Supportive, hands-on culture: Flat hierarchy, guidance from senior engineers, and a hybrid setup that balances focus time with in-person collaboration.
