Prompt Engineer / LLM Application Developer

# Prompt Engineer / LLM Application Developer **Experience:** 2–5 years ## Role Overview We are looking for a **Prompt Engineer / LLM Application Developer** with hands-on experience building production-ready applications powered by Large Language Models (LLMs). The ideal candidate will combine strong prompt and context engineering skills with full-stack development expertise to build and integrate AI-powered features into real-world product workflows. The role requires a strong focus on **AI-native development**, including the effective use of AI coding assistants and modern LLM development practices. ## Key Responsibilities * Design, test, optimize, and iterate prompts and context strategies for production AI features. * Build and integrate LLM-powered application features end-to-end, from frontend interfaces to backend APIs. * Develop reusable prompt libraries, templates, and evaluation datasets for consistent and reliable LLM outputs. * Establish and maintain evaluation processes to measure and improve the quality, accuracy, and performance of LLM-based features. * Use AI-native development tools such as **Cursor and Claude Code** as part of the regular software development workflow. * Integrate LLM APIs and AI capabilities into existing and new product workflows. * Maintain prompt and model configuration versioning to ensure reproducibility and transparency. * Collaborate with product, engineering, and other technical teams to translate business requirements into AI-powered solutions. * Troubleshoot and optimize LLM application performance, including prompt efficiency, context management, and output quality. ## Required Skills & Experience * 2–5 years of software development experience with recent hands-on experience delivering LLM-powered applications. * Strong practical experience with **LLM APIs**, particularly **OpenAI and/or Anthropic**. * Strong understanding of **prompt engineering, context engineering, and LLM application development**. * Full-stack development experience with technologies such as: * **React / TypeScript** * **Node.js and/or Python** * Hands-on experience using AI coding assistants such as **Cursor and Claude Code**. * Experience with LLM evaluation frameworks, automated evaluations, or regression testing for AI outputs. * Understanding of API development, integrations, and modern software development practices. * Strong problem-solving and debugging skills. * Ability to work effectively in a fast-paced, AI-first development environment. ## Nice to Have * Experience in gaming, entertainment technology, or similar consumer-facing products. * Experience working with AI-native development methodologies. * Familiarity with LLM observability, monitoring, and performance optimization. * Experience designing scalable prompt and evaluation frameworks. ## Ideal Candidate The ideal candidate is a hands-on developer who can move beyond experimentation and **build, evaluate, and deploy LLM-powered features in production**. They should be comfortable combining software engineering, prompt engineering, and AI-native development tools to deliver reliable application-level AI solutions. ## Role Overview We are looking for a **Prompt Engineer / LLM Application Developer** with hands-on experience building production-ready applications powered by Large Language Models (LLMs). The ideal candidate will combine strong prompt and context engineering skills with full-stack development expertise to build and integrate AI-powered features into real-world product workflows. The role requires a strong focus on **AI-native development**, including the effective use of AI coding assistants and modern LLM development practices. ## Key Responsibilities * Design, test, optimize, and iterate prompts and context strategies for production AI features. * Build and integrate LLM-powered application features end-to-end, from frontend interfaces to backend APIs. * Develop reusable prompt libraries, templates, and evaluation datasets for consistent and reliable LLM outputs. * Establish and maintain evaluation processes to measure and improve the quality, accuracy, and performance of LLM-based features. * Use AI-native development tools such as **Cursor and Claude Code** as part of the regular software development workflow. * Integrate LLM APIs and AI capabilities into existing and new product workflows. * Maintain prompt and model configuration versioning to ensure reproducibility and transparency. * Collaborate with product, engineering, and other technical teams to translate business requirements into AI-powered solutions. * Troubleshoot and optimize LLM application performance, including prompt efficiency, context management, and output quality. ## Required Skills & Experience * 2–5 years of software development experience with recent hands-on experience delivering LLM-powered applications. * Strong practical experience with **LLM APIs**, particularly **OpenAI and/or Anthropic**. * Strong understanding of **prompt engineering, context engineering, and LLM application development**. * Full-stack development experience with technologies such as: * **React / TypeScript** * **Node.js and/or Python** * Hands-on experience using AI coding assistants such as **Cursor and Claude Code**. * Experience with LLM evaluation frameworks, automated evaluations, or regression testing for AI outputs. * Understanding of API development, integrations, and modern software development practices. * Strong problem-solving and debugging skills. * Ability to work effectively in a fast-paced, AI-first development environment. ## Nice to Have * Experience in gaming, entertainment technology, or similar consumer-facing products. * Experience working with AI-native development methodologies. * Familiarity with LLM observability, monitoring, and performance optimization. * Experience designing scalable prompt and evaluation frameworks. ## Ideal Candidate The ideal candidate is a hands-on developer who can move beyond experimentation and **build, evaluate, and deploy LLM-powered features in production**. They should be comfortable combining software engineering, prompt engineering, and AI-native development tools to deliver reliable application-level AI solutions.