Hire Pre-vetted Prompt Engineers

What is a Prompt Engineer?

A Prompt Engineer is a specialist who designs, tests, and optimizes prompts for large language models (LLMs) like GPT-4, Claude, and others—creating inputs that elicit high-quality outputs from AI models. Prompt Engineers do more than write simple queries—they understand how LLMs work, design effective prompts that achieve specific goals, evaluate output quality, and refine approaches iteratively. Whether you need someone to build prompt-based systems into your products, optimize AI tool usage, or explore AI capabilities, a skilled Prompt Engineer brings creativity, analytical thinking, and AI literacy.

What makes Prompt Engineers valuable is their ability to unlock AI capabilities effectively. They understand that how you ask matters enormously for AI systems. They design prompts that generate consistent, high-quality outputs and know when to try different approaches versus settling on suboptimal results. This is why forward-thinking organizations invest in Prompt Engineers. When you hire through Torc, you're getting someone who helps you leverage AI effectively.

Technology Stack

LLM Platforms & APIs

• OpenAI API (GPT-4, GPT-3.5)

• Anthropic Claude API

• Google's Gemini API

• Open-source models (Llama, Mistral)

Prompt Design Tools

• Prompt engineering platforms (Prompt.com, LangSmith)

• LLM testing & evaluation frameworks

• Jupyter notebooks for experimentation

• Version control for prompts

Evaluation & Metrics

• Output quality evaluation frameworks

• Automated evaluation tools

• A/B testing approaches for prompts

• Bias detection & fairness assessment

Integration & Deployment

• LLM APIs integration

• Retrieval-augmented generation (RAG)

• Embedding models & vector databases

• Application frameworks (LangChain, LlamaIndex)

Domain Knowledge

• Understanding of LLM capabilities & limitations

• Knowledge of different model architectures

• Understanding of context windows & token limits

• Few-shot learning & prompt techniques

Key Qualities to Look For on a Prompt Engineer

LLM Expertise — They understand how LLMs work, what prompts they respond to well, and how to structure inputs for consistent outputs. They know different models' strengths.

Experimentation Mindset — They hypothesize about prompt approaches, test systematically, evaluate results, and iterate. They balance exploration with pragmatism.

Quality Thinking — They care about output quality. They evaluate responses critically, identify when outputs are good versus suboptimal, and refine until consistent.

Business Understanding — They understand how AI solutions create business value. They know what success looks like and design prompts accordingly.

Documentation & Communication — They document prompts clearly, explain why approaches work, and communicate findings to stakeholders.

Continuous Learning — LLM capabilities evolve rapidly. The best prompt engineers stay current with new models, techniques, and emerging patterns.

Project Types Your Prompt Engineers Handle

Prompt Optimization — Designing and optimizing prompts for specific tasks. Real scenarios: Customer service chatbot optimization, content generation optimization, code generation optimization.

System Design — Building prompt-based systems into products. Real scenarios: AI-powered features in applications, automated workflows, content generation pipelines.

Evaluation Framework Development — Building frameworks to evaluate LLM outputs. Real scenarios: Quality assessment frameworks, bias detection, performance benchmarking.

RAG Systems — Building retrieval-augmented generation systems. Real scenarios: Document Q&A systems, knowledge base systems, contextual AI assistants.

Multi-Model Strategy — Evaluating and comparing different LLM models. Real scenarios: Model comparison analysis, optimal model selection, hybrid approaches.

Fine-Tuning & Customization — Fine-tuning models or designing custom prompts. Real scenarios: Industry-specific model optimization, domain-specific fine-tuning, specialized use cases.

Integration & Deployment — Integrating LLMs into applications. Real scenarios: API integration, embedding models in products, monitoring & evaluation systems.

Interview questions

Question 1: "Tell me about a complex task you solved with prompt engineering. What was the challenge, how did you design the prompt, and what iterations did you go through?"

Why this matters: Tests prompt design and iterative refinement capability. Reveals whether they understand LLM behavior, can diagnose failures. Shows practical prompt engineering experience.

Question 2: "Describe a time you had to evaluate and compare different LLM models. How did you approach it and what factors influenced your decision?"

Why this matters: Tests model selection skills and understanding of LLM landscape. Reveals whether they choose blindly or evaluate systematically. Shows business acumen about trade-offs.

Question 3: "Tell me about a prompt engineering system or application you built. How did you handle prompt versioning, evaluation, and deployment?"

Why this matters: Tests systems thinking about prompts as code. Reveals whether they treat prompts professionally or casually. Shows production-ready approach.