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.