Software Engineering
Engineering the Infrastructure of AI.
Specializing in Cloud-Native Go, Kubernetes, and scalable pipelines for Model Inference and Distillation.
Focus & Research
Kubernetes for AI Workflows
Architecting and maintaining specialized Kubernetes clusters designed to support end-to-end AI lifecycles. This research focuses on designing robust infrastructure for model training and inference, ensuring seamless scalability and resource efficiency for data-intensive operations.
LLM Distillation & Optimization
Investigating emerging trends in Large Language Model capabilities, with a specific emphasis on model distillation. The primary objective is transforming large, general-purpose models into efficient, domain-specific solutions suitable for specialized high-performance use cases.
AI-Enhanced Software Engineering
Evaluation and application of diverse AI models and tooling within the software development lifecycle. Research focuses on bridging the gap between rapid prototyping and production-ready implementations, ensuring a rigorous balance between computational efficiency, code clarity, and long-term system maintainability.
Core Technologies
Go (Golang)
Building high-performance, concurrent backend systems and resilient microservices.
Kubernetes
Orchestration at scale. Designing operators and managing complex container lifecycles.
Cloud Native
Architecting distributed systems that are elastic, observable, and loosely coupled.
AI Pipelines
Infrastructure for training and serving models. Integrating AI into production workflows.
Linux
Deep system-level expertise, kernel tuning, and automation via shell scripting for high-performance environments.
Security
Implementing secure-by-design principles, container hardening, and robust network policies.
Connect
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