Summary
Farid Haziyev is a Senior Machine Learning Engineer with a decade of experience specializing in NLP, speech, and applied ML systems, currently focused on LLMs, RAG, and production-grade model deployment. He has built end-to-end solutions—from NER and HS-code classifiers to multimodal valuation networks—and deployed scalable inference services using FastAPI, Docker, MLflow, Prefect and W&B. His work spans speech technologies (fine-tuning Wav2Vec, Azerbaijani ASR/TTS) and practical entity resolution pipelines that combine TF-IDF with NER, reflecting a rare blend of research rigor and production pragmatism. At TTEK and previous roles he leveraged prompt engineering and LangChain to extract compliance and risk insights from unstructured data, while creating Streamlit interfaces to make experiments accessible to stakeholders. With a strong academic background from Hacettepe University and METU and proven success in multilingual and domain-specific ML, he excels at turning complex language and audio problems into reliable, scalable services.
10 years of coding experience
5 years of employment as a software developer
Master's degree, Computer Engineering, 3.84, Master's degree, Computer Engineering, 3.84 at Hacettepe Üniversitesi
Bachelor of Science (BS), Industrial Engineering, Bachelor of Science (BS), Industrial Engineering at Orta Doğu Teknik Üniversitesi / Middle East Technical University
English, Russian, Turkish