Hire NLP Engineer — text systems with evaluation, not vibes

Both, where each wins. Rules handle the predictable patterns cheaply and transparently; ML handles the messy long tail. I start with rules as a baseline, then add models only where they measurably beat them.

Arabic needs dedicated handling: morphology-aware tokenization, diacritic normalization, dialect coverage, and code-switching with English. I evaluate on your actual text — Gulf dialect support tickets behave nothing like news articles.

Usually substantially. Domain-tuned embeddings plus hybrid keyword-semantic search and reranking typically beat generic search on relevance — measured on your queries with a labeled eval set, so improvement is proven, not promised.

Task-specific metrics on a labeled test set you own — precision/recall for extraction, relevance judgments for search — plus error analysis showing exactly where the model fails. If it can’t be measured, it can’t be improved.

Your corpus stays yours. I work within your infrastructure or under NDA, redact PII before any external API touches text, and prefer self-hosted models where privacy rules them out entirely.

I work fractionally on a day-rate or fixed-scope basis, not as a body-shop headcount. Most engagements start with a paid one-to-two week diagnostic — data audit, baseline, and a costed plan — so you commit real money to a small risk before a large one. Build phases are then scoped as fixed deliverables with clear acceptance criteria. You are paying for senior judgement that avoids six-figure mistakes, not hours filled.

A credible baseline and an honest assessment of difficulty inside the first two weeks — that is the point of the diagnostic. A production-ready classifier or extraction pipeline with monitoring is typically four to eight weeks depending on how clean your labelled data is. If you already have good labels, it is faster; if annotation has to be built from scratch, that becomes the critical path and I will tell you so upfront rather than discover it later.

It depends on volume, latency, and how open-ended the task is, which is exactly the decision you hire an NLP engineer to make correctly. High-volume, narrow tasks favour a fine-tuned encoder for cost and speed. Open-ended reasoning over your documents favours retrieval-augmented generation with grounded sources. Low-volume, varied tasks can live on a prompted LLM. I prototype the contenders against your data and let the measured cost-accuracy tradeoff decide, not fashion.

Yes — inheriting half-built or undocumented systems is a large part of what I do. I first write the evaluation and tests that should have existed, so we can measure the current system honestly before changing it. Then I decide with you whether to stabilise and extend it or migrate cleanly. I will not rip out working code for the sake of a rewrite; the test harness tells us what is actually worth replacing.

Yes. I am based in Dubai and have delivered for clients across the UAE, US, UK, Canada, Pakistan, and Malaysia, so overlapping with North American mornings or European afternoons is routine. I work asynchronously by default — written specs, versioned experiments, and readable dashboards — so progress does not depend on a live call. When you hire an NLP engineer remotely, the real risk is opacity, so I keep the work visible rather than behind a curtain.

A full-time senior NLP engineer is expensive, slow to recruit, and often under-utilised once the hard problems are solved. An agency gives you a rotating cast and a markup. I give you one accountable senior who scopes tightly, builds the system, and deliberately hands it to your team with documentation and tests. You get the judgement of fifteen-plus years and a hundred-plus projects for the months you actually need it, then you stop paying.