Machine Learning Systems & AI Infrastructure
I build production machine learning systems. My work covers data pipelines, training, inference, and the platform layer behind document understanding, computer vision, and generative AI applications.
I am joining turbalance as an incoming Software Engineer on May 18, 2026. I will work on AI trace generation for distributed workloads, helping make large-scale AI systems easier to observe, replay, and optimize.
Previously, I built core systems for document processing and enterprise automation at Multimodal, and computer vision systems and machine learning models in healthcare and robotics.
Work
My work sits at the intersection of machine learning and systems engineering. I focus on the infrastructure behind AI applications: serving, orchestration, tracing, reliability, latency, and cost.
Starting May 18, 2026
I will work on systems that capture and analyze how distributed AI workloads run across GPUs and clusters. The focus is trace collection, instrumentation, and simulation for LLM inference and training systems, with the goal of finding bottlenecks and improving performance at scale.
Enterprise-grade multi-agent orchestration platform for document processing, unstructured data workflows, decision automation, enterprise search, conversational systems, and reporting. Built for multi-tenant production workloads with containerized services, automated delivery, and reliability work across the stack.
Platform
Workflow builder that turns business requirements into configured AI systems. I designed and developed core pieces for natural language workflow creation, ingestion monitoring, confidence scoring, audit trails, and integration with the broader AgentFlow platform.
Blog postProjects
A Go server for using OpenAI APIs with custom server-side features and streaming data to the frontend through gRPC.
A graphical interface for training pre-trained deep learning models, with simplified setup and real-time progress monitoring through TensorBoard.
Research
I am interested in machine learning infrastructure and bio-inspired AI models. Much of my research is about turning deep learning models into efficient, scalable systems.
IEEE, 2023 8th International Conference on Computer Science and Engineering
IEEE, 2022 7th International Conference on Computer Science and Engineering
Education
2020 - 2024
Algorithms, data structures, object-oriented programming, database systems, operating systems, computer architecture, computer networks, software engineering, discrete mathematics, probability, statistics, and digital system design.
Beyond Code
Outside of building AI systems, you will find me on the track, specifically the Nürburgring Nordschleife. I am drawn to sim racing and the challenge of perfecting every apex and braking point.
I read science fiction and dive into neuroscience papers when I can. The intersection of biological and artificial intelligence shapes how I think about building better models.
On AGI: I believe we are further away than the hype suggests. Reinforcement learning shows promise in specific domains, but it is not a silver bullet. The path forward requires rigorous research, not just scaling existing methods.