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.