AI Engineer

What is an AI Engineer?

An AI Engineer builds AI-powered products and features — integrating large language models (LLMs), designing AI pipelines, building RAG systems, and creating the infrastructure that makes AI work reliably in production. Unlike a machine learning engineer who trains models, an AI engineer primarily works with pre-trained models (OpenAI, Anthropic, open-source LLMs) and builds the product layer on top: prompt engineering, retrieval systems, agent architectures, and evaluation frameworks. In 2026, the AI engineer is one of the most in-demand roles in tech.

‍

At what stage should you hire an AI Engineer?

Immediately at AI-native companies. For companies adding AI capabilities to existing products, the right time is Series A or whenever AI becomes a core product bet — not an experiment. The signal: you have a clear use case where AI creates real user value, and the bottleneck is engineering capacity to build and ship it.

‍

Common titles for this role

• AI Engineer
• LLM Engineer
• Generative AI Engineer
• Applied AI Engineer
• AI/ML Engineer
• AI Software Engineer

‍

Typical background

Strong AI engineers combine solid software engineering fundamentals with deep LLM product experience. RFS has placed AI engineers at Windsurf (AI coding), Mercor (AI recruiting), Decagon (AI customer support), and other top AI-native companies. We look for engineers who have shipped AI features to real users — with evaluation frameworks, prompt versioning, and production observability — not just weekend hackathon demos.

‍

What does an AI Engineer do at a startup?

• Design and implement LLM-powered features: chat interfaces, agents, document analysis, code generation
• Build RAG (Retrieval Augmented Generation) systems with vector databases (Pinecone, Weaviate, pgvector)
• Engineer and version prompts: system prompts, few-shot examples, chain-of-thought patterns
• Implement AI evaluation frameworks: accuracy benchmarks, hallucination detection, regression tests
• Optimize LLM inference: latency, token cost, caching strategies
• Integrate AI model APIs: OpenAI, Anthropic Claude, open-source models via Ollama or Hugging Face
• Build AI agent architectures: tool use, multi-step reasoning, memory systems

‍

Key skills and qualifications

• Strong software engineering fundamentals: Python, TypeScript, production-quality code
• Deep LLM API experience: OpenAI, Anthropic, or comparable — not just surface-level API calls
• RAG architecture: embedding generation, vector storage, retrieval strategies, reranking
• Prompt engineering and evaluation: measurable quality benchmarks, regression prevention
• AI infrastructure: inference optimization, streaming responses, context management
• Understanding of AI failure modes: hallucination, context limits, prompt injection

‍

Why hire your AI Engineer through RFS?

• We've placed AI engineers at Windsurf, Mercor, Decagon, and Scale AI — our network reaches the best AI talent in 2026
• We distinguish between AI engineers who've shipped production AI and those who've only experimented — you only see the former
• 29-day average time to hire — AI engineer searches are among the most competitive; our warm network is the advantage
• Pre-vetted for production LLM experience including evaluation frameworks and observability
• No upfront fees — contingency model