AI Field Engineer(FDE)

SINGDATA CLOUD PTE. LTD.; Singapore; Presencial; SGD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Singdata is building an industry-leading AI-Native data platform and application ecosystem in Singapore. As a member of Singdata's Singapore AI Innovation Center, you will be at the forefront of Data + AI technologies—creating enterprise-grade intelligent applications with transformative impact.

You will lead scenario-driven development, design LLM-based engines, and implement RAG architectures while collaborating with product and platform teams to ship reliable, user-focused solutions.

Formación

• Strong CS fundamentals in data structures, algorithms, OS, and networks.
• Experience integrating ML/LLM with data tech stacks (Spark, Flink, Trino, Doris).
• Hands-on experience with at least two of: LLM app development, ElasticSearch/RAG, LangChain/LlamaIndex.
• Product-focused mindset and user-centric approach.
• Strong learning and communication skills.

Responsabilidades

• Scenario-driven AI application development across enterprise scenarios.
• Develop LLM-based conversational engines and RAG systems with data integration.
• Drive PoC to productization with cross-team collaboration.

Conocimientos

CS fundamentals

Python

Java

C++

Go

ML/LLM basics

Spark

Flink

Trino

Doris

ElasticSearch

LangChain

LlamaIndex

Communication

Herramientas

Spark

Flink

Trino

Doris

ElasticSearch

LangChain

LlamaIndex

Descripción del empleo

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

1. 1.Scenario-Driven Application Development

   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. 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. 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

• 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