AI Field Engineer(FDE)

Singdata inc.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Singdata Inc. in Singapore is building an AI-native data platform and application ecosystem at the Singapore AI Innovation Center. You will help shape enterprise-grade AI applications by blending data capabilities with cutting-edge AI technologies.

Role focuses on designing AI app architectures, implementing RAG and agent-like systems, and driving PoC to productization in collaboration with cross-functional teams. Fluency in English and Mandarin is a plus.

Qualifications

• Strong CS fundamentals in data structures, algorithms, OS and networks.
• Proficiency in Python/Java/C++/Go and at least one mainstream language.
• Hands-on experience integrating ML/LLM with modern data stacks (Spark, Flink, etc.).
• Familiarity with AI app architectures: LLM development, RAG, and vector stores.
• Product-minded with ability to translate tech into user value and outcomes.
• Strong learning agility and clear technical communication across teams.

Responsibilities

• Understand enterprise scenarios, identify core pain points, and translate to AI solutions.
• Lead design and development of scenario-specific AI applications, e.g., risk control and data insight tools.
• Develop architectures for AI apps: LLMs, RAG, and data integrations, with end-to-end ownership.
• Drive PoC to productization in collaboration with product, algorithm, and platform teams.

Skills

CS fundamentals

Python/Java/C++/Go

Data & AI integration

AI application stack

Product mindset

Learning & communication

Tools

Spark

Flink

Trino

Doris

ElasticSearch

Vector DBs

LangChain

LlamaIndex

Job description

At Singdata, we are building an industry-leading AI-Native data platform and application ecosystem. As a member of Singdata’s Singapore AI Innovation Center, you will be at the forefront of Data + AI technologies—combining our powerful data platform capabilities with cutting‑edge AI technologies to create enterprise‑grade intelligent applications with transformative impact.

Responsibilities

Deeply understand enterprise business scenarios, identify core pain points, and translate them into technical solutions. You will lead the design and development of scenario‑specific AI applications, such as intelligent risk control systems, data insight assistants, and more.

2.Understanding Core Architectures of AI Applications

Develop a solid understanding of the following AI application architectural components:

• Conversational & Task Frameworks: Build LLM-based conversational engines and agent frameworks capable of autonomously executing complex data tasks.
• RAG Engine Optimization: Design and implement efficient, accurate Retrieval-Augmented Generation (RAG) systems with deep integration of structured and unstructured data.
• Data-Driven Insight Tools: Develop intelligent analysis tools that automatically detect data patterns, anomalies, and trends.

3.Engineering Excellence & Productization

Drive the process from technology selection and PoC development to full productization. Work closely with product, algorithm, and platform teams to transform technical vision into reliable, user‑delighting products.

Requirements (Who You Are)

• Strong Computer Science Fundamentals: Solid understanding of data structures, algorithms, operating systems, and networks. Proficient in at least one mainstream programming language (Python/Java/C++/Go).
• Experience in Combining Data & AI: Knowledge of machine learning or LLM fundamentals, plus hands‑on experience integrating them with modern data tech stacks (e.g., Spark, Flink, Trino, Doris).
• Understanding of the AI Application Tech Stack: Familiarity with at least two of the following, with hands‑on implementation experience:
• LLM application development, including prompt engineering and fine‑tuning
• Search systems (ElasticSearch, Vector DBs) and RAG architectures
• Agent frameworks (LangChain, LlamaIndex) or custom task‑planning engines
• Product & User‑Centric Mindset: Passionate about creating user value. Able to zoom out from technical implementation and shape product experience from the user’s perspective. Measure technical success by product outcomes.
• Learning & Communication Skills: Ability to learn continuously in a fast‑evolving field. Excellent communication skills to articulate complex technical concepts and collaborate across teams.

Nice‑to‑Have

• Proficiency in both Chinese and English
• AI application development experience in industries such as finance, insurance, or retail
• Experience with MLOps or DataOps, including model deployment, monitoring, and iteration
• Contributions to open‑source communities or active technical blogging