About the Role
We are looking for experienced Applied AI Engineers, AI Engineers, and Generative 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 LLM-based applications, AI agent workflows, retrieval-augmented generation (RAG) systems, prompt engineering, and AI-generated code and outputs.
Selected professionals will work on tasks that require a strong understanding of modern AI architectures, generative AI frameworks, LLM integrations, agentic workflows, and practical implementation of AI-powered applications.
The objective is to improve the accuracy, reasoning, performance, and reliability of AI systems by generating, reviewing, annotating, and evaluating technically complex datasets and AI-generated outputs.
Key Responsibilities
- AI Application Analysis: Review and understand existing AI-powered applications, analyse implementation logic, evaluate system architecture, and assess technical correctness.
- LLM Architecture Evaluation: Analyse large language models, transformer-based architectures, model configurations, and LLM integration workflows.
- RAG Pipeline Evaluation: Assess retrieval-augmented generation systems, embedding models, vector databases, retrieval mechanisms, and context-generation workflows.
- AI Agent Evaluation: Evaluate AI agent architectures, tool-calling mechanisms, multi-agent workflows, orchestration logic, and autonomous task execution.
- Technical Data Annotation: Perform high-quality annotations on AI-related datasets, including prompts, responses, code, agent workflows, and technical documentation.
- AI-Generated Output Evaluation: Assess AI-generated outputs for factual accuracy, logical reasoning, contextual relevance, instruction adherence, and technical correctness.
- Prompt Engineering & Evaluation: Review and evaluate prompts, system instructions, prompt chains, and AI-generated responses for quality and effectiveness.
- Code-Level Assessment: Review Python-based AI implementations, identify logical errors, evaluate integration approaches, and suggest technically sound corrections.
- AI Model Integration & Fine-Tuning: Evaluate LLM APIs, model integrations, fine-tuning approaches, inference pipelines, and model deployment workflows.
- AI Framework Evaluation: Assess implementations built using frameworks such as LangChain, LlamaIndex, and other AI application development frameworks.
- 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 Applied AI Engineering, AI Engineering, Generative AI, or related technical roles.
- Strong hands-on experience with Python.
- Practical experience building or maintaining LLM-powered applications.
- Strong understanding of LLMs, transformer-based architectures, and generative AI concepts.
- Experience working with LLM APIs such as OpenAI, Anthropic, Gemini, or open-source models.
- Practical experience with Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
- Familiarity with AI agent frameworks, tool calling, and workflow orchestration.
- Experience with frameworks such as LangChain, LlamaIndex, or similar technologies.
- Understanding of prompt engineering, context management, and LLM evaluation methodologies.
- Familiarity with model fine-tuning, inference pipelines, and AI application deployment.
- Ability to independently read, understand, and evaluate existing AI application 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 2 hours of dedicated work per day.
- Part-time engagement with flexible working hours, subject to project deadlines and task requirements.
- Hourly-based payment for completed work, as per project-specific compensation terms.
- Professionals must be available consistently and meet the expected quality and delivery standards.
Ideal Candidates
This opportunity is suitable for working Applied AI Engineers, Generative AI Engineers, LLM Engineers, AI Application Developers, and professionals with practical experience in building, integrating, evaluating, or maintaining AI-powered applications and systems.
