Arya Farhang

Background

About Arya Farhang

From writing code that connects users to interfaces, to developing AI systems that exceed human performance - my journey reflects my passion for solving complex technical challenges that make a real impact. Starting as a full-stack developer in 2017, I discovered my true calling in Machine Learning, fascinated by its potential to push the boundaries of what's possible. This led me to transition into ML engineering, where I've since specialized in computer vision, natural language processing, and MLOps, working on projects ranging from autonomous vehicle systems at Volvo Cars to clinical diagnostics at Enaiblers. Currently, as an ML Engineer at Enaiblers, I'm driving both technical innovation and business value. My key achievements include- Architecting scalable ML infrastructure on Azure that unlocked $50K in credits through efficient resource utilization - Optimizing ML model inference latency from 1300ms to 400ms on edge devices - Developing production-ready ML pipelines that achieved above-human performance in clinical settings - Implementing and optimizing large language models for various applications My experience spans the full ML lifecycle - from cloud infrastructure and MLOps to model development and deployment. I combine my software engineering background with ML expertise to build systems that are not only technically sophisticated but also cost-effective and scalable. Particularly passionate about- Cloud-native ML systems and cost optimization - Natural Language Processing and Large Language Models - Computer Vision applications in healthcare - Edge AI deployment and optimization - Building scalable, production-ready ML pipelines I believe in the power of AI to transform industries, whether through computer vision in healthcare, NLP in business processes, or edge AI in industrial applications. My approach combines technical excellence with business acumen, ensuring solutions that deliver both performance and value. Technical expertise: PyTorch, TensorFlow, Hugging Face, LangChain, Azure ML, Docker, Kubernetes, MLflow Languages: English, Persian (Native), Swedish (Working), German (Elementary) Let's connect if you're interested in discussing ML engineering, cloud architecture, or the future of AI!"