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Hire me
NLP / ML engineer specializing in speech-to-text and low-resource
languages. I take language and speech models from notebook to production
— fine-tuning, ONNX optimization, APIs, deployment, and load-testing — and I
do it for languages the big cloud providers handle poorly or not at all.
Computational linguist by training (M.Sc., University of Stuttgart,
2017), with a decade building NLP, speech-to-text, and MLOps systems across
industry and research.
Availability
Open to full-time roles — as an employee or a long-term, full-time
contractor. I'm looking for a team I can commit to and grow with, not a
string of short gigs.
Remote, worldwide — or on-site in the EU or the Balkans. My
working hours are flexible and provide strong overlap with both European and
US-East business hours. I'm open to relocation.
How I can help
- Speech-to-text for your language or domain
- Fine-tune, evaluate, and deploy automatic speech recognition (Whisper
and others), including languages not supported out of the box. Honest
WER/CER benchmarking, not vendor hand-waving.
- ML models from notebook to production
- Take a working model and make it deployable and affordable: ONNX
conversion and quantization, a FastAPI service, Docker, load-testing, and
cost optimization. (At ProWritingAid, ONNX conversion and quantization let
a workload the team had scoped for 8 GPU machines run on 1 — plus a warm
standby for redundancy — at negligible accuracy loss.)
- NLP data pipelines at scale
- Scrape, clean, and structure large text corpora for NLP/LLM training
and search — for example, an end-to-end ingestion pipeline over 20M+ USPTO
trademark records feeding a multi-layered Elasticsearch similarity
engine.
- Low-resource & multilingual NLP
- Morphological analysers, machine translation, data collection, and
evaluation for Turkic, Caucasian, and other under-resourced languages.
Eight publications and several shipped open-source tools in this
area.
Selected results
- Shipped ProWritingAid's premium-tier tone-detection feature —
training, fine-tuning, Azure ML deployment, and ONNX optimization.
- Built an end-to-end pipeline over 20M+ USPTO trademark records with
phonetic, orthographic, and cross-lingual similarity search.
- Fine-tuned Coqui STT for Kazakh (arXiv:2107.10637)
and Whisper for Laz, and launched Common Voice for Kazakh and
Tatar.
Get in touch
The fastest way to start is a short message describing what you're
building and the outcome you need.
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