What is an AI developer?
An AI developer is a software engineer who builds production systems that use machine-learning models — typically large language models (LLMs) like Claude, GPT-4, or open-source Llama / Mistral. The work spans LLM integration (calling Anthropic / OpenAI APIs), retrieval-augmented generation (RAG) systems, AI automation workflows, fine-tuning, self-hosted-LLM deployments, and AI agent architectures. An AI developer differs from an ML engineer in that the former builds systems on top of trained models; the latter trains models from scratch.
The longer answer
The AI-developer role has crystallized into a distinct discipline since 2023. It splits into roughly three sub-specialties. LLM integration developers build customer-facing features and internal tools by wrapping commercial APIs (OpenAI, Anthropic) — chatbots, document-summarization, content-generation, classification. RAG and agent developers build systems where the LLM is one component in a larger architecture — retrieving relevant context from vector databases, calling tools, coordinating multi-step workflows. Self-hosted-LLM and fine-tuning developers deploy open-source models (Llama, Mistral, DeepSeek) on commodity GPUs and fine-tune them for domain-specific tasks — usually for compliance, cost, or data-privacy reasons.
What an AI developer is NOT
Three common confusions. First, an AI developer is not necessarily an ML engineer. Most AI developers consume models trained by others (Anthropic, OpenAI, Meta) rather than training new ones. Second, an AI developer is not a data scientist. Data scientists do statistical analysis, build predictive models, and run experiments; AI developers ship production systems. Third, an AI developer is not a prompt engineer. Prompt engineering is a small part of the work; the larger part is everything around the prompt — retrieval, context management, tool use, evaluation harnesses, observability.
The senior-AI-developer skill spike
Three signals beyond LLM-API fluency: evaluation discipline (running automated evals on every change, not just smoke-testing), observability (Langfuse / OpenTelemetry / Arize for AI workloads — knowing what a query cost, what it returned, what it should have returned), and cost-and-latency awareness (knowing when to use Claude 4.7 vs Haiku, when to batch, when to cache, when to switch to a self-hosted Llama 3.x). The third is what separates a $20/month MVP from a $5,000/month production system.
Common follow-up questions
AI developer vs ML engineer — which do I need?
An AI developer if you want to ship a feature using existing models (chatbots, document AI, content generation, automation). An ML engineer if you need a custom-trained model for a domain-specific task that the major commercial models cannot handle.
What is RAG?
Retrieval-Augmented Generation — a pattern where you store domain-specific documents in a vector database, retrieve the most-relevant passages for a given query, and pass them as context to an LLM. RAG is the dominant pattern for building AI features over private corporate knowledge.
Should I use OpenAI or Anthropic?
Anthropic's Claude models lead on writing quality, instruction-following, and long-context reasoning; OpenAI's GPT models lead on speed-of-iteration in the ecosystem and have broader third-party tooling. For most business workloads either works; the load-bearing decision is usually downstream — evals, observability, and cost monitoring.
If this answer is useful and you have a real engagement in mind, the contact form routes directly to the principal — James Henderson is the single engineer who scopes, writes, and supports every engagement end-to-end.