“We don’t need AI researchers. We need builders who know what to do with models once they’re in production.”
This issue of Redeployed is brought to you by Tecla: AI is no longer a side project. It’s part of the job. As models move into production, engineering leaders are hiring for people who can wire them into real workflows, ship fast, and own the outcome when things break. Tecla helps teams hire senior nearshore engineers who understand AI in context, not in theory. Builders who operate inside the system, move with urgency, and know what it takes to make AI work in practice.
This month, Tecla reviewed its most common client requests. Not just for roles, but for outcomes. What leaders are hiring for. What they’re reorganizing around. What they actually expect from their teams in 2026.
It all points in one direction: AI is no longer a special project. It is a product requirement.
Below are the three most frequent asks from engineering heads across industries and what they tell us about where AI is headed.
01. “We want AI inside the workflow, not floating above it.”
The novelty phase is over. Most teams have already integrated a model or two. What they want now are engineers who can take those capabilities and wire them into real systems. Think vector search in internal tools. Retrieval-augmented generation layered into support flows. In-product assistants that do more than summarize.
What leaders are asking for are not ML engineers. They are systems engineers who know what AI is good at and how to contain what it is not.
02. “MVPs in weeks, not quarters.”
AI is compressing product timelines. With LLMs now writing basic UI and scaffolding logic, founders and PMs are asking their teams to move faster and they are hiring accordingly. Candidates who can take an idea and ship something usable in a sprint or two are getting prioritized over specialists who can only contribute once the spec is written.
This has also changed how teams view risk. One fintech startup told us their tolerance for low-fidelity MVPs has gone up, because the feedback cycle is so much faster now. Shipping is no longer the finish line. It is the start of training.
03. “We need domain fluency, not just dev fluency.”
AI is forcing teams to re-center context. Leaders in industries like healthcare, logistics, and legal tech are asking for candidates with vertical-specific experience. Not because the code is different. Because the stakes are. Knowing when not to ship a hallucination is now part of the job.
As AI models get more general, companies are seeking people who can make them specific. That means product engineers who understand the workflows, not just the APIs. And it means hiring for pattern recognition inside the domain, not just inside the code.
What This Means for 2026
AI is no longer being adopted. It is being operationalized. That shift is subtle but significant. It moves the pressure from exploration to integration. From tooling to orchestration. From promise to proof.
The shift from AI as a prototype to AI as infrastructure is showing up in hiring. Engineering leaders aren’t just asking for AI-literate devs. They want builders who can move fast, understand context, and co-own outcomes. That’s why teams are scaling with LATAM engineers who already speak the language of modern stacks, AI included. Senior-level, English-proficient, and in your time zone.
And as always, the companies who figure that out first are the ones who build faster, not just with AI, but with people who know how to work with it.
More to come…
Recommended Reads
✔️ AI Skills Are Reshaping Wages and Hiring Decisions — World Economic Forum
✔️ AI in the Workforce: Leadership Strategies for 2026 — GFoundry
✔️ What AI in the Workforce Actually Means for Your Organization — Gartner AI Workforce FAQ
– Gino Ferrand, Founder @ Tecla