$ whoami
10+
Years Experience
5M+
Users at Scale
200+
Microservices
100%
Uptime Focus
Eric Skogman
AI, DevOps & Full-Stack Engineer
From 0 → Production at global scale
Bare metal → Cloud → Edge. Zero-downtime deployments. Always shipping.
From bare metal servers to edge functions
From Kubernetes clusters to React frontends
From ML models to production APIs
From Kubernetes clusters to React frontends
From ML models to production APIs
Co-founder @flarebase_dev
Antler Cohort 17
Founder University #10
$ git log --all --oneline | wc -l
0
Total Commits
0
Day Streak
0
Longest Streak
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$ cat bio.txt
Eric Skogman is a seasoned DevOps and Full-Stack Engineer specializing in building and scaling high-availability systems at the intersection of infrastructure and AI. With deep expertise in cloud orchestration, Kubernetes, and modern full-stack development, Eric combines infrastructure mastery with MLOps capabilities to deploy real-time AI applications at global scale. Certified in Machine Learning, he architects edge-native serverless platforms, implements LLM inference pipelines, and manages vector embeddings infrastructure for production AI workloads.
- Kubernetes-based platform engineering with GitOps (ArgoCD) for zero-downtime deployments across bare metal, cloud VMs, and hybrid environments
- Edge-native serverless architectures on Cloudflare Workers for global real-time AI applications
- AI Agent Orchestration: Prompt engineering multi-LLM workflows for rapid prototyping and development, reducing dev cycles by 5x
- MLOps & AIOps: Deploying/scaling deep learning models (CNNs, Transformers, LLMs) in production pipelines
- Full-stack development with modern frameworks (React, Remix, NexyJS, Vue, Node, Rails)
- Multi-region AWS/GCP infrastructure with comprehensive observability and DevSecOps practices
$ git log --experience
Senior DevOps Engineer
Monad Foundation
Mar 2025 - Jan 2026 · 11 mos
Remote · Contract
- Architected and maintained Kubernetes infrastructure on bare metal servers for high-performance blockchain network processing 1000+ TPS
- Managed hybrid infrastructure across bare metal validators and AWS cloud nodes with automated failover and disaster recovery
- Implemented comprehensive observability stack achieving 99.95% uptime across distributed validator nodes
- Optimized CI/CD pipelines with GitHub Actions, reducing deployment time by 60% for rapid testnet iterations
Entrepreneur in Residence
Antler
Sep 2024 - Dec 2024 · 4 mos
Singapore
- Accepted into highly selective startup incubator (less than 3% acceptance rate)
- Validated product-market fit for edge-native serverless platform through market research and customer interviews
- Built initial product architecture and technical foundation for Flarebase
- Developed go-to-market strategy and early customer acquisition pipeline
Founder - Cohort #10
- Backed by Founder University, joining an elite cohort of technical founders building venture-scale companies
- Accelerated Flarebase development with mentorship from successful founders and investors
- Refined technical architecture and scaling strategy for global edge deployment
- Built investor network and developed fundraising materials
Co-founder & CTO
- Building edge-native serverless backend platform with <50ms global latency for real-time LLM inference and AI applications
- Architected MLOps pipeline for vector embeddings with 10,000+ queries/sec throughput across 300+ global edge locations
- Deep integration with Cloudflare ecosystem: Workers for compute, Durable Objects for stateful coordination, R2 for storage, D1 for edge SQL, KV for caching
- Implemented real-time data syncing and multi-database support (PostgreSQL, Redis, vector DBs) with 99.99% availability
- Leveraging Cloudflare Workers infrastructure for cost-efficient global deployment, reducing latency by 80% vs traditional cloud
░ KEY ACHIEVEMENT
Cloudflare Startup Program
$250k+ Credits
Accepted into Cloudflare's prestigious Startup Program, securing over $250,000 in infrastructure credits. Enabling global-scale edge-native deployment with sub-50ms latency for real-time LLM inference and AI workloads across 300+ locations worldwide.
Senior DevOps Engineer
Exodus
Mar 2021 - Jan 2024 · 2 yrs 11 mos
Remote
- Managed multi-region AWS infrastructure supporting 5+ million users with 99.98% uptime SLA across EC2, EKS, RDS, S3, CloudFront, and Lambda
- Scaled 200+ microservices on Kubernetes (EKS and self-managed clusters on EC2) with GitOps (ArgoCD), achieving zero-downtime deployments and reducing release cycles by 70%
- Architected hybrid cloud/bare metal infrastructure for high-performance workloads requiring dedicated compute resources
- Built internal DevOps tooling that improved developer productivity by 40% and reduced infrastructure costs by 30% through AWS cost optimization and rightsizing
- Implemented comprehensive observability with Grafana, Prometheus, and ELK processing 100TB+ logs/month
"Eric performed his work with high precision, great quality, and strong focus on customer benefit. He shares his knowledge generously and has a great will to constantly learn more—a tremendous asset to any team."
Software Engineer
Loom Network
Nov 2017 - Nov 2019 · 2 yrs 1 mo
- Created dashboard and wallet application for staking cryptocurrencies, processing over $5 million in transactions with 10,000+ active users
- Built production-ready dApps on one of the first Proof of Stake sidechains, handling 500+ TPS with sub-second finality
- Architected full-stack P2P marketplace for ERC721 tokens with real-time order matching and on-chain settlement
- Developed React/TypeScript frontend and Node.js backend integrated with Ethereum smart contracts
"Eric's unique combination of DevOps expertise and blockchain knowledge allowed us to deploy complex decentralized systems with confidence."
$ cat publications.bib
February 2025
Synthetic Face Embeddings: Research Notes and Methodology
Introduced EigenFace, a fully synthetic face embedding model trained exclusively on AI-generated faces, achieving 91% accuracy on LFW benchmark. Released the EigenFace-256 dataset, a multi-terabyte synthetic face dataset with controlled variations in pose, illumination, expression, and age progression—addressing privacy concerns and demographic bias in traditional face recognition systems.
- 91% LFW accuracy using 100% synthetic training data (ResNet-100 architecture)
- Multi-terabyte dataset with controlled variations: angles, lighting, expressions, age progression
- Commercially permissive licensing for open research and production use
- Ethically sound approach eliminating privacy concerns with real-world datasets
- Reduces demographic bias through balanced synthetic data generation
- Explored 6 state-of-the-art synthetic generation methods: DCFace, SynFace, DiscoFaceGAN, Arc2Face, Flux
$ ls -la ./skills/
AWS Ecosystem (Advanced)
EC2, EKS, ECS, Lambda, RDS, S3, CloudFront, VPC, IAM, Route53, CloudWatch, Systems Manager
Cloudflare Platform (Expert)
Workers, Durable Objects, R2, D1, KV, Pages, CDN, DNS, WAF, Tunnel, Stream, Images
DevOps & Platform Engineering
Kubernetes (Bare Metal, EKS, Self-Managed), Docker, Terraform, GitHub Actions, ArgoCD, Flux (GitOps)
Infrastructure Management
Bare Metal Servers, Cloud VMs (EC2, GCE), Hybrid Cloud, Network Architecture, Load Balancing, Auto-scaling
MLOps & AI Infrastructure
LLM Deployment, Vector Databases (Pinecone, Weaviate), Embedding Pipelines, Real-time Inference, Edge AI
Observability & Security
Grafana, Prometheus, ELK Stack, Trivy, Snyk, OPA, DevSecOps, Cost Optimization (FinOps)
Full-Stack & Languages
TypeScript, Python, Golang, Ruby, React, Vue, Node.js, Rails, PostgreSQL, Redis
Machine Learning (Certified)
LLMs, CNNs, RNNs, Custom Model Training, Vector Embeddings, YOLO, DQN, MADDPG