Data Annotator - Applied AI Engineer
New
J
JobgetherArtificial intelligence
Work from anywhere in IndiaPart-TimeMiddle
Salary30 - 80 USD per hour
Apply NowOpens the employer's application page
Job Details
- Experience
- 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.
- Required Skills
- PythonPrompt EngineeringGenerative AILangChain
Requirements
- Have 2–6 years of professional experience, including at least 2 years in Applied AI Engineering, AI Engineering, Generative AI, LLM Engineering, or a related technical field.
- Have strong hands-on programming experience with Python.
- Have practical experience building, integrating, or maintaining applications powered by large language models.
- Understand LLMs, transformer-based architectures, and generative AI concepts.
- Have experience with LLM APIs and models, including OpenAI, Anthropic, Google Gemini, or open-source alternatives.
- Have practical knowledge of RAG, embeddings, and vector databases.
- Be familiar with AI agent frameworks, tool calling, multi-agent systems, and workflow orchestration.
- Have experience with LangChain, LlamaIndex, or comparable AI application development frameworks.
- Understand prompt engineering, context management, and LLM evaluation methodologies.
- Be familiar with model fine-tuning, inference pipelines, and AI application deployment processes.
- Be able to independently read, understand, analyze, and evaluate existing AI application codebases.
- Have experience using Git/GitHub and collaborative software development workflows.
Responsibilities
- Analyze AI-powered applications, review implementation logic, assess system architectures, and evaluate technical correctness.
- Evaluate large language models, transformer-based architectures, model configurations, and LLM integration workflows.
- Assess 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.
- Annotate 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.
- Examine Python-based AI implementations, identify logical errors, and assess integration approaches.
- Evaluate LLM APIs, model integrations, fine-tuning strategies, inference pipelines, and AI model deployment workflows.
- Produce structured annotations, technical explanations, justifications, and actionable feedback for AI model evaluation and improvement.
- Maintain annotation accuracy, consistency, and quality while following project-specific guidelines and evaluation criteria.
View Full Description & ApplyYou'll be redirected to the employer's site
