I am an innovative and impact-driven Machine Learning Engineer with almost a decade of experience across research and industry, delivering scalable NLP, LLM, and Generative AI systems. I have a proven success in building and deploying end-to-end ML pipelines β from data ingestion to real-time inference β with measurable business results, including 90%+ reductions in processing time and multi-million asset-scale deployments. My skills include model development, MLOps, cloud-native architectures (AWS, Azure), and production-grade software engineering using Python, PyTorch, and modern ML frameworks. As a published researcher, I have a strong foundation in algorithmic thinking, experimentation, and system design.
GPA: 3.71/4.0; Dissertation: Image to Music: Cross-Modal Melody Generation through Image Captioning
Improved character identity consistency in multi-frame generations by fine-tuning LoRA models (kohya ss) and optimizing prompts; designed scalable workflow to automate training runs across GPU nodes. Built and deployed 20+ character-specific LoRA models using ComfyUI, cutting training cycles by 30% through curated dataset pipelines and minimal-step design. Owned and integrated a theme-based video generation API, enabling scalable, scriptable production of 100+ short videos used in downstream storytelling and animation workflows. Collaborated with product managers to define success metrics and iterate quickly.
Designed and delivered a scalable ML pipeline for resume parsing and ranking using transformers, LLMs, and classical models; reduced selection time from 3 days to 2 hours per 100 applicants. Took full ownership of architecture β including tokenizer logic, JSON output design, model deployment, and vector-based ranking system β ensuring modularity and reusability across HR teams. Led development of a production-grade chatbot to automate regional support, reducing manual requests by 40.
Spearheaded development of ML models for NFT price estimation, combining collection-based and global models to handle asset-level variability across major marketplaces. Achieved 90%+ T+1 prediction accuracy on millions of NFTs; optimized batch inference and latency for production API, ensuring real-time pricing capability. Mentored junior engineers and oversaw mid-level contributors, reviewing code, guiding experimentation, and enforcing best practices across modeling and data workflows.
Built and deployed NLP models for multilingual sentiment analysis, topic clustering, query understanding, and expansion; deployed features in AppGallery search at multi-million user scale. Collaborated with PMs and search infrastructure teams across 4 languages (Arabic, English, Spanish, Russian); designed modular APIs and lightweight pipelines for zero-downtime integration.
Preprocessed EEG data and developed ML models for cognitive signal analysis; supported instruction for graduate-level cognitive science courses.
Built and published a real-time Android messaging app (4.4/5.0, 1,000+ downloads) with custom emoji support and IRC-based backend.
Developed an image matching system using deep learning, reducing matching time by 91%; co-authored a related peer-reviewed paper.
Created an Android learning app for Turkcell Academy with animations, level-flow, and SQLite integration.
Jobicy
571 professionals pay to access exclusive and experimental features on Jobicy
Free
USD $0/month
For people just getting started
Plus
USD $8/month
Everything in Free, and: