Data Scientist (AI Platform Applications)

FPT Asia Pacific; Singapore; On-site; SGD 100,000 - 167,000; Full time

Job summary

FPT Asia Pacific is seeking a contract Data Scientist to extend in-house AI platforms into new business use cases and ship platform improvements. This role focuses on integration and deployment rather than research.

You'll write production-grade Python code, work with data engineers, and translate stakeholder requirements into tangible features while reusing platform components where possible.

Qualifications

• Degree in computer science or data science; PhD not required.
• 3+ years hands-on experience building production software with some LLM/GenAI work.
• Strong software engineering fundamentals, Python proficiency, API design and cloud deployment (AWS).
• Experience with at least one LLM framework/SDK (LangChain, LangGraph, AWS AgentCore, GPT/Claude/Gemini APIs).
• Able to work independently in a Kanban delivery environment with minimal ramp-up time.
• Time series analysis and traditional ML experience is a bonus.

Responsibilities

• Apply existing in-house AI platform capabilities to new business use cases; map to platform primitives and build integration.
• Enhance and extend platform products: add integrations, improve workflows, extend eval/monitoring coverage, close gaps.
• Write production-grade code (Python required; Node.js/TypeScript a strong plus) for services, pipelines, and APIs.
• Collaborate with data engineers and platform team lead to understand system constraints before extending the system.
• Translate stakeholder requirements into working features, prioritizing reusing existing platform components.
• Contribute to internal documentation and knowledge transfer for future maintenance.

Skills

LLM/GenAI

Python

API design

AWS cloud deployment

Production software

Kanban delivery

Education

Bachelor's degree in computer science/data science/related field

Tools

AWS

LangChain

LangGraph

GPT/Claude/Gemini APIs

Job description

Job Description

We arelooking for a contract Data Scientist to extend our in-house AI platforms into new business use cases, and to enhance these products based on evolving requirements.

This is a build-and-apply role, not a research role. You'll spend most of your time integrating existing capabilities into new stakeholder use cases and shipping improvements to the platforms themselves.

Key Responsibilities

• Apply existing in-house AI platform capabilities to new business use cases brought in by stakeholders across various business units — scoping the use case, mapping it to existing platform primitives, and building the integration.
• Enhance and extend current platform products: add new integrations, improve workflows, extend evaluation/monitoring coverage, and close gaps identified through production usage.
• Write production-grade code (Python required; Node.js/TypeScript a strong plus) for services, pipelines, and APIs that plug into the existing platform architecture.
• Work directly with data engineers and the platform team lead to understand system design constraints before extending the system.
• Collaborate with business stakeholders to translate requirements into a working feature — with a bias toward reusing existing platform components over building bespoke ones.
• Contribute to internal documentation and knowledge transfer so use cases you build can be maintained by the core team after handover.

Requirements

• Degree in computer science, data science, or related field. PhD not required — this role is evaluated on shipped work, not research output.
• 3+ years hands-on experience building production software, ideally including some LLM/GenAI application work (RAG, agents, tool-calling, prompt engineering).
• Strong software engineering fundamentals: clean Python, comfortable reading/extending an existing codebase, decent grasp of API design and cloud deployment (AWS preferred).
• Practical experience with at least one LLM framework or SDK (Google ADK, AWS AgentCore, LangChain, LangGraph, or direct API integration with GPT/Claude/Gemini) — depth of software engineering ability matters more than familiarity with a specific framework.
• Comfortable working within an existing platform's architecture and conventions rather than designing systems from scratch.
• Experience with time series analysis and traditional ML (forecasting, regression, classification, feature engineering) is a bonus, not a requirement.
• Able to work independently in a Kanban-style delivery environment with minimal ramp-up time.