Hire NLP Engineer — text systems with evaluation, not vibes
NLP fails quietly: extractors that miss your product names, sentiment that misreads sarcasm, embeddings that drift as language changes. I start with evaluation — labeled test sets, error analysis, baselines — before touching a model. NER tuned to your vocabulary, multilingual pipelines handling Arabic morphology and mixed-language text, retrieval grounded in your corpus.
I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. I’ve built search, classification, and chat systems on messy real-world text — see my computer vision developer and OpenAI API developer pages for related builds.
NLP engineered around your language
Named entity recognition
Extractors trained on your entities — products, people, places, contract terms — with annotation guidelines and agreement metrics, so the model learns your vocabulary instead of guessing at generic labels.
Sentiment & classification
Classifiers for sentiment, intent, topic, and toxicity tuned to your domain’s language — with calibrated confidence scores and a human-review queue for the borderline cases models shouldn’t decide alone.
Embeddings & semantic search
Domain-tuned embeddings with hybrid search and reranking over your corpus — so search understands meaning, not just keywords, and returns answers with sources your team can verify.
Multilingual & Arabic NLP
Pipelines that handle Arabic morphology, diacritics, dialects, and code-switching with English — tokenization, normalization, and models evaluated on your actual text, not Modern Standard Arabic alone.
Evaluation harnesses
Labeled test sets, regression suites, and error dashboards that run on every model change — the infrastructure that turns “the model feels worse” into a measurable, fixable regression.
LLM-assisted annotation
Using large models to pre-label data with human verification — cutting annotation cost and time while keeping a human in the loop where accuracy actually matters.
Measured progress on your text
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Corpus & task audit
I read your actual text — tickets, reviews, documents — and define the NLP tasks precisely: what gets extracted, classified, or searched, and what “good” means in numbers.
Baseline & annotation
A simple baseline first, then annotation guidelines and a labeled eval set — so every later improvement is measured against something real, not a feeling.
Model build & tuning
Domain-tuned models trained and validated on your eval set — with error analysis driving each iteration toward the mistakes that actually cost you.
Deploy & monitor
APIs with latency budgets, confidence thresholds, and human-review queues — plus scheduled re-evaluation as your language and data drift over time.
Why hire through a fractional CTO
NLP work often stops at a notebook with a good F1 score. Production is where it breaks — new slang, new products, new formats. I build for that: eval sets that grow with your data, models retrained on schedule, pipelines your team operates. Fifteen years of production work, reliability included.
Dubai-based, working worldwide across 6 countries and 100+ projects. Arabic and multilingual text is a specialty, not an afterthought. Tell me what your text needs to do.
NLP engineer FAQs
Rule-based or machine learning — which approach?
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.
How do you handle Arabic text?
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.
Can you improve our search?
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.
How do you measure quality?
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.
What about data privacy for our text?
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.
Make your text work harder
Send sample documents or describe the task — I’ll scope the approach, the eval plan, and the timeline.