Why Work With Me
Infrastructure Excellence for AI Systems
I specialize in building the foundational infrastructure that AI teams depend on—robust compute clusters, efficient MLOps platforms, and scalable deployment pipelines that turn research into production.
Scalable Infrastructure
Design and deploy GPU clusters, distributed training systems, and inference platforms that handle production workloads.
MLOps Platforms
Build complete MLOps infrastructure with experiment tracking, model registries, and automated deployment pipelines.
Production Reliability
Implement monitoring, observability, and automation that keeps AI systems running smoothly at scale.
Services
What I Can Do For You
GPU Cluster Design & Deployment
Build high-performance compute infrastructure for training and inference, from single-node setups to distributed multi-GPU clusters.
MLOps Platform Engineering
Deploy complete MLOps platforms with experiment tracking, model registries, orchestration, and automated deployment pipelines.
Model Serving Infrastructure
Design and implement scalable inference systems with load balancing, auto-scaling, and low-latency serving for production workloads.
Data Infrastructure
Build data lakes, feature stores, and ETL pipelines optimized for ML workloads with proper versioning and lineage tracking.
CI/CD for ML Systems
Implement automated testing, validation, and deployment pipelines specifically designed for ML models and infrastructure.
Monitoring & Observability
Deploy comprehensive monitoring for infrastructure metrics, model performance, data quality, and system health at scale.
Infrastructure in Action
Real-time visualization of production AI systems
ML Pipeline Flow
GPU Cluster
Real-time node status
GPU Utilization
Last 60 seconds
Active Training Jobs
Technical Expertise
Technologies & Tools
Compute & Orchestration
MLOps & Automation
Model Serving & Inference
Cloud & Infrastructure
Experience
Building AI Infrastructure at Scale
AI Infrastructure Engineer
Independent Consultant
Building production-grade AI infrastructure for organizations scaling their ML operations—from GPU clusters to complete MLOps platforms.
- • Designed and deployed distributed GPU training infrastructure across multiple clouds
- • Built MLOps platforms serving 100+ models in production with 99.9% uptime
- • Optimized inference infrastructure reducing latency by 70% and costs by 50%
DevOps Engineer
Previous Experience
Built and maintained cloud infrastructure, Kubernetes platforms, and automation systems at scale.
- • Managed multi-region Kubernetes clusters serving production traffic
- • Implemented infrastructure-as-code across AWS and Azure environments
- • Built CI/CD pipelines and observability platforms for distributed systems
Systems Engineer
Earlier Career
Focused on infrastructure automation, reliability engineering, and operational excellence.
- • Automated infrastructure provisioning and configuration management
- • Improved system reliability and reduced incident response time by 60%
- • Developed monitoring, logging, and alerting infrastructure
How I Work
A Proven Approach to AI Infrastructure
Infrastructure Assessment
Evaluate current infrastructure, identify bottlenecks, and define requirements for scale.
Architecture Design
Design compute clusters, storage systems, and MLOps platforms optimized for your workloads.
Platform Build
Deploy infrastructure with IaC, configure orchestration, and implement automation pipelines.
Optimization & Monitoring
Fine-tune performance, implement observability, and establish operational best practices.
Ready to Scale Your AI Infrastructure?
Let's discuss how I can help you build robust, scalable infrastructure that accelerates your AI initiatives and supports production workloads.