Why AI Engineer roles will outnumber FDEs, and why vendor optionality matters

Summary

Andrew Ng argues that whilst the AI Forward Deployed Engineer (FDE) role is expanding as vendors like OpenAI and Anthropic build FDE teams, most firms will hire far more AI Engineers — their own staff — than embedded vendors. He frames the client concern around vendor lock-in: when the best AI platform is uncertain, tightly binding a firm's processes to one vendor reduces optionality. He predicts the AI Engineer role will fragment into specialisations (LLMOps, Evals, AI Data, Harness Engineering) as Software Engineering did decades ago, but generalist AI Engineers who blend LLM prompting, agentic frameworks, evals, and coding-agent tooling are in high demand now. Read the original.

Key Claims

• AI Engineer roles will far outnumber FDE roles; most companies will hire more of their own staff than they will accept embedded vendor engineers.
• Tight vendor integration reduces flexibility when the best platform choice is uncertain; clients worry about being locked into the wrong vendor.
• Demand is surging for generalist AI Engineers who combine LLM prompting, agentic frameworks, evals, and AI coding agents (Claude Code, Codex, etc.).
• The AI Engineer role will fragment into specialisations — LLMOps, Evals, AI Data, Harness Engineering — similar to how Software Engineering split into frontend, backend, mobile, and data engineering.

Quotes

• "A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects."
• "In this moment when it's hard to predict which AI service will be the best one in a year's time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable."