Data Annotator - Applied AI Engineer

Accountabilities:: Analyze existing AI-powered applications, review their implementation logic, assess system architectures, and evaluate technical correctness. Evaluate large language models, transformer-based architectures, model configurations, and LLM integration workflows. Assess Retrieval-Augmented Generation (RAG) pipelines, including embedding models, vector databases, retrieval mechanisms, and context-generation processes. Review AI agent architectures, tool-calling mechanisms, multi-agent workflows, orchestration logic, and autonomous task execution. Perform specialized technical data annotation involving prompts, AI-generated responses, code, agent workflows, and technical documentation. Evaluate AI-generated outputs for factual accuracy, logical reasoning, contextual relevance, instruction adherence, and technical quality. Review and assess prompt engineering techniques, system instructions, prompt chains, and AI-generated responses. Examine Python-based AI implementations, identify logical errors, assess integration approaches, and recommend technically sound improvements. Evaluate LLM APIs, model integrations, fine-tuning strategies, inference pipelines, and AI model deployment workflows. Assess AI applications developed using frameworks such as LangChain, LlamaIndex, and other generative AI development technologies. Produce clear technical explanations, structured annotations, justifications, and actionable feedback to support AI model evaluation and improvement. Maintain high standards of annotation accuracy, consistency, and quality while following project-specific guidelines and evaluation criteria. Requirements: 2–6 years of professional experience, with at least 2 years in Applied AI Engineering, AI Engineering, Generative AI, LLM Engineering, or a related technical field. Strong hands-on programming experience with Python. Practical experience building, integrating, or maintaining applications powered by large language models. Strong understanding of LLMs, transformer-based architectures, and generative AI concepts. Experience working with LLM APIs and models, including OpenAI, Anthropic, Google Gemini, or open-source alternatives. Practical knowledge of Retrieval-Augmented Generation (RAG), embeddings, and vector databases. Familiarity with AI agent frameworks, tool calling, multi-agent systems, and workflow orchestration. Experience with LangChain, LlamaIndex, or comparable AI application development frameworks. Understanding of prompt engineering, context management, and LLM evaluation methodologies. Familiarity with model fine-tuning, inference pipelines, and AI application deployment processes. Ability to independently read, understand, analyze, and evaluate existing AI application codebases. Experience using Git/GitHub and collaborative software development workflows. Strong analytical thinking, technical reasoning, and problem-solving abilities. Excellent written communication skills, with the ability to clearly explain technical decisions, findings, and evaluation results. Ability to work independently, maintain consistent availability, and deliver accurate, high-quality results within project deadlines. Suitable professional backgrounds include Applied AI Engineers, Generative AI Engineers, LLM Engineers, and AI Application Developers. Benefits: Competitive hourly compensation: USD 30–80, depending on project-specific terms and experience. Fully remote working arrangement, allowing you to work from anywhere in India. Flexible part-time engagement with a minimum commitment of 2 hours per day. Flexible working hours, subject to project deadlines and delivery requirements. Opportunity to work on advanced AI projects involving LLMs, generative AI, RAG systems, and intelligent agents. Hands-on exposure to modern AI technologies, frameworks, and model evaluation methodologies. Opportunity to apply and expand your technical expertise while contributing to the development of more accurate and reliable AI systems. Project-based work offering flexibility alongside other professional commitments.