Professional Overview
I am a visionary AI Engineer and Architect specializing in Multi-Agent, Multimodal RAG with LLM orchestration, renowned for building GPU-accelerated, cloud-native platforms that deliver ultra-low latency and high accuracy for scalable, production AI solutions. My unique value proposition lies in transforming complex AI challenges into tangible, quantifiable business outcomes.I don't just build; I architect and deploy. For instance, I engineered AutonoMind AI, a production-grade Multi-Agent RAG system featuring a custom MCP framework enabling low-latency orchestration across 6+ modular agents. My MathGPT platform achieved 7 ms/token ultra-low latency inference and an industry-leading 99.9% correctness on 50,000+ algorithmic challenges via QLoRA fine-tuning and NVIDIA A100-powered GCP GKE infrastructure. This isn't just theory; I accelerated CFD simulations by 90% with >98% accuracy in my Masterarbeit, boosting operational efficiency by 25% through ML-driven mechanism reduction.I am the rare talent who bridges cutting-edge research with robust, deployable engineering, driving multi-million dollar value through innovation in Generative AI, MLOps, and HPC. Employers should consider me not just for my deep technical acumen in Python, LangChain, Kubernetes, and Terraform, but for my proven ability to deliver transformative, measurable results that directly impact the bottom line.
Areas of Expertise
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
data science & analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
