Rather than train generalists, we develop the specific capabilities behind the two AI roles companies are working hardest to fill — the ones broad, generic courses tend to overlook.
What it is: Part engineer, part problem-solver — embeds with customers to take frontier AI from demo to production. Palantir, who pioneered the role, describes it as "similar to those of a startup CTO." OpenAI, Anthropic, and a wave of AI companies now hire them fast.
Why it's exploding: Only about 5% of enterprise AI pilots reach production. FDEs are the people who close that gap.
Why it's hard to fill: It demands a rare blend — full-stack/ML engineering, LLM and RAG fluency, cloud skills, and the ability to operate autonomously in an unfamiliar codebase in front of a customer. That's exactly what we build.
What it is: The engineer who builds the data backbone of AI — ingestion pipelines, vector databases, embeddings, retrieval (RAG) infrastructure, and the governance that makes LLM apps safe and reliable in production.
Why it's in demand: McKinsey ranks software and data engineers as the most in-demand AI hires. As companies move AI from pilots to production, this role is the bottleneck.
Why it's hard to fill: Most candidates have either traditional data engineering (no LLM exposure) or ML research (no production depth). The combined skill set barely existed two years ago — so we teach it directly.
Demand isn't the question. Being qualified is. That's the gap DolfynAI closes.