AI Engineer (Amk)

AI Systems Engineer Objective: To support the Project Lead and AI Architecture Lead in the implementation, optimisation, and sustained execution of AI-enabled service automation systems, including AI runtime behaviour, data preparation pipelines, and operational readiness. Key Responsibilities: Support end-to-end execution of AI-enabled service automation projects from implementation through operational validation. Design, implement, and maintain data preparation and transformation pipelines for structured and unstructured data (e.g. documents, logs, sensor data) used in AI systems. Build and maintain ETL/ELT workflows to ingest, clean, transform, and validate data for model training, tuning, RAG pipelines, and inference. Support preparatory data work for model training, fine-tuning, tuning cycles, and RAG optimisation. Implement, integrate, and optimise deployed AI and ML components within broader software systems. Execute solution testing to ensure functional and non-functional requirements are met. Conduct performance and stress testing of AI components, interpret results, and generate reports to support optimisation. Support integration with existing frontend or alternative visualisation layers where required, without frontend ownership responsibility. Assist with deployment readiness, UAT/SAT support, and issue diagnosis during system integration. Skills and Qualifications: Minimum education: Education in Engineering, Computer Science, Applied AI or related technical discipline, or equivalent practical experience or skills-based training. Practical experience implementing or supporting AI or ML systems in applied, non-research environments. Working knowledge of programming in Python for AI and data processing workflows Familiarity with AI, ML, or model inference frameworks and libraries. Basic understanding of data pipelines, data transformation, and data quality concepts. Ability to understand and work with software logic flows, system architectures, and decision trees. Strong communication and teamwork skills. Ability to work independently with technical guidance. Ability to produce clear technical documentation and reports in English. Preferred Qualifications: Experience supporting RAG pipelines, model inference, or AI system optimisation. Exposure to hardware acceleration or AI optimisation toolchains (e.g. CUDA, TensorRT, OpenVINO, or equivalent) maestro HR damien lee tian hong R1106726 16C8462