3 posts tagged with "engineering-leadership"

Three months after rolling out an AI coding assistant to their entire engineering team, a company noticed something disturbing: their code review pass rate had dropped 18%, their sprint velocity was up, but the number of production incidents had climbed. When they asked developers to explain a recent AI-generated module during a post-mortem, nobody in the room could. Not even the person who merged it.

This is the cognitive offloading trap. And it's not a failure of AI tools — it's a failure of how teams integrate them.

Most engineering teams that hire for LLM roles run roughly the same interview: two rounds of LeetCode, a system design question, maybe a quiz on transformer internals. They're assessing for the wrong things — and they know it. The candidates who ace those screens often struggle to ship working AI features, while the ones who stumble on binary search can build an eval suite from scratch and debug a hallucinating pipeline in an afternoon.

The skills that predict success in LLM engineering have almost no overlap with what traditional ML or software interviews test. Hiring managers who haven't updated their process are generating false negatives at a high rate — rejecting engineers who would succeed — while false positives walk in with solid LeetCode scores and no intuition for when a model is confidently wrong.

Three years ago, "AI team" meant a group of specialists tucked into a corner of the org chart, mostly invisible to product engineers. Today, a senior software engineer at a fintech company ships a fraud-scoring feature using a fine-tuned model on Monday, wires up a RAG pipeline for customer support on Wednesday, and debugs LLM latency on Friday. The specialists didn't go away—but the boundary between "AI work" and "product engineering" dissolved faster than almost anyone planned for.

Most teams responded by bolting new titles onto existing job descriptions and calling it done. That's the wrong answer, and the dysfunction shows up quickly: unclear ownership, duplicated tooling, and an ML platform team that spends half its time explaining why product teams can't just call the OpenAI API directly.

This post is about getting the structure right—not in the abstract, but for the actual stages of AI adoption most engineering organizations go through.