Data Annotator - Machine Learning Engineer

About the Role We are looking for experienced Machine Learning Engineers, Applied ML Engineers, and AI Engineers to contribute to specialised data annotation, technical evaluation, and AI model training projects. This is not a conventional data annotation role. The work involves advanced technical evaluation of machine learning concepts, code, model architectures, and AI-generated outputs. Selected professionals will work on tasks that require a strong understanding of ML engineering, modern AI architectures, and practical implementation workflows. The objective is to help improve the quality, accuracy, and reliability of AI models by generating, reviewing, annotating, and evaluating technically complex datasets. Key Responsibilities ML Codebase Analysis: Review and understand existing machine learning codebases, identify model components, analyse implementation logic, and evaluate code quality and correctness. Model Architecture Evaluation: Analyse transformer-based architectures, LLM implementations, model configurations, and machine learning workflows. Technical Data Annotation: Perform high-quality annotations on ML-related datasets, including code, model outputs, implementation steps, and technical documentation. AI-Generated Output Evaluation: Assess outputs generated by AI models for technical accuracy, logical consistency, implementation correctness, and adherence to given requirements. Code-Level Assessment: Review Python and ML code, identify logical errors, evaluate implementation approaches, and suggest technically sound corrections. Model Training & Fine-Tuning Evaluation: Assess fine-tuning workflows, training configurations, inference pipelines, and model evaluation approaches. Prompt and Response Evaluation: Analyse prompts and AI-generated responses for technical relevance, accuracy, completeness, and adherence to instructions. Documentation & Reasoning: Provide clear technical explanations, annotations, justifications, and structured feedback for evaluation tasks. Quality Assurance: Maintain high annotation accuracy and consistency while following project-specific guidelines and evaluation standards.. Required Skills & Qualifications 2+ years of professional experience in Machine Learning Engineering, Applied ML, AI Engineering, or related technical roles. Strong hands-on experience with Python. Practical experience with ML frameworks such as PyTorch. Experience working with Hugging Face Transformers and modern LLM architectures. Strong understanding of transformer-based models, model fine-tuning, inference, and evaluation. Familiarity with prompt engineering and LLM-based applications. Ability to independently read, understand, and evaluate existing ML codebases. Experience with Git/GitHub and collaborative software development workflows. Strong analytical, problem-solving, and technical reasoning abilities. Excellent written communication skills to explain technical decisions and evaluation outcomes. Time Commitment Minimum 3 hours of dedicated work per day. 6 working days per week. Part-time engagement with flexible working hours, subject to project deadlines and task requirements. Professionals must be available consistently and meet the expected quality and delivery standards. Ideal Candidates This opportunity is suitable for working ML Engineers, AI Engineers, Applied Scientists, and professionals with practical experience in building, evaluating, or maintaining machine learning systems.