Professional Overview
Staff-level Software and AI Platform Engineer with 10+ years building backend, cloud, Kubernetes, and distributed systems. I specialize in AI infrastructure, LLM serving, GPU workloads, and production platforms using Go, Python, AWS, vLLM, Terraform, and NVIDIA tooling. Open to high-impact remote contract and consulting work.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Own AI platforms across cloud/on-prem: Kubernetes, LLM serving, and GPUs.
- Built a Go control plane for LLM lifecycle, GPU scheduling, and multi-tenancy.
- Operate Kubernetes with AWS EKS, RKE2, Terraform, ArgoCD, Helm, and Kustomize.
- Built GPU infrastructure using vLLM, NVIDIA GPU Operator, and DCGM.
- Own B2B authentication and enterprise SSO with Better Auth, OIDC, and SAML.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Led platform engineering, infrastructure, authentication, and enterprise SSO.
- Migrated Django authentication to AWS Lambda and Cloudflare Workers services.
- Built a Workers API gateway; improved cloud reliability, CI/CD, and recovery.
Key Achievements / KPIs
- Reduced authentication component costs by 80% and latency by 60%.
- Cut deployment time from 30 to 15 minutes across environments.
- Improved API gateway performance by 40% with Cloudflare Workers.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Backend Developer (03/2018-10/2019): built Django/BigQuery B2B analytics.
- Technical Lead (10/2019-07/2020): Go microservices and GCP-to-AWS migration.
- Head of Engineering (07/2020-11/2021): led strategy with 40+ engineers.
- Led architecture reviews, mentoring, engineering standards, and delivery.
Key Achievements / KPIs
- Reduced infrastructure costs by eighty percent via GCP-to-AWS migration.
Education
Field of Study: Electrical Engineering
Field of Study: Electrical Engineering
Field of Study: Electrical Engineering
Areas of Expertise
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
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.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
