LLM, GenAI Engineer

Location: Singapore
Work Mode: Onsite
Employment Type: Full-time
Experience Level: Junior–Mid

Role Overview

We are looking for an AI & Data Engineer with hands-onexperience in Large Language Models, GenAI application development, andcloud-based data engineering. The candidate should be self-driven, curious, andcomfortable independently building end-to-end solutions, from data ingestionand preparation to LLM integration and deployment.

Key Responsibilities

• Design, build, and enhance LLM-driven applications and frameworks.
• Implement Retrieval-Augmented Generation, AI agents, and intelligent workflows.
• Work with LLM tooling and runtimes such as Llama.cpp, Ollama, and similar ecosystems.
• Build data ingestion, transformation, and ETL/ELT pipelines for structured and unstructured data.
• Work with cloud data platforms such as Microsoft Fabric, OneLake, Lakehouse, or equivalent technologies.
• Prepare and process data for AI applications using Python, SQL, Spark, or PySpark.
• Develop and maintain backend services and APIs using Python.
• Integrate LLM applications with databases, vector stores, APIs, and enterprise data sources.
• Research, prototype, and evaluate emerging AI models, frameworks, and data technologies.
• Continuously improve solution accuracy, performance, scalability, and usability.

Required Skills & Qualifications

• Strong understanding of LLMs and GenAI applications.
• Knowledge of RAG, embeddings, vector search, and agentic workflows.
• Familiarity with LLM frameworks and tooling such as Llama.cpp, Ollama, LangChain, LangGraph, Semantic Kernel, or equivalent.
• Proficiency in Python and SQL.
• Understanding of data engineering concepts, including ETL/ELT, data pipelines, data modelling, and data quality.
• Exposure to Microsoft Fabric, OneLake, Lakehouse, Azure Data Factory, Databricks, or an equivalent cloud data platform.
• Familiarity with REST APIs, databases, and Git.
• Ability to independently translate ideas into working technical solutions.
• Academic background in Artificial Intelligence, Computer Science, Data Engineering, or a related discipline.

Nice to Have

• Experience running or deploying LLMs locally, on servers, or in cloud environments.
• Exposure to vector databases, semantic search, and enterprise knowledge retrieval.
• Knowledge of prompt engineering and LLM evaluation techniques.
• Experience with Spark, PySpark, Dataflows, or cloud-based data pipelines.
• Familiarity with Docker, CI/CD, Azure OpenAI, Azure AI Search, or other cloud AI services.