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I build AI-ready platforms as stacked capabilities: each layer builds on the one below, creating a foundation that serves both analytics and ML teams.
The foundation: Kubernetes clusters, infrastructure as code, GitOps workflows, observability, security baselines, and golden paths that enable teams to ship with confidence.
EKS, AKS, GKE clusters with auto-scaling, security policies, and multi-tenancy
Terraform, Pulumi, Crossplane for declarative, version-controlled infrastructure
ArgoCD, Flux, GitHub Actions for automated, auditable deployments
Prometheus, Grafana, OpenSearch for metrics, logs, traces, and alerting
Data foundations on cloud and Kubernetes: lakehouse/warehouse architecture, streaming pipelines, data quality, governance, and cost-efficient storage/compute for analytics and ML.
Databricks, Delta Lake, medallion architecture for unified analytics
OpenSearch, Elasticsearch clusters with optimized indexing and query performance
Kafka, Spark Streaming, real-time data ingestion and transformation
Unity Catalog, access controls, lineage tracking, compliance
Infrastructure for the ML lifecycle: feature pipelines, training workflows, experiment tracking, model registry, and production deployment with monitoring and safe rollouts.
Kubeflow, Argo Workflows for reproducible training and feature engineering
MLflow, model versioning, hyperparameter tracking, artifact management
Seldon Core, KServe for A/B testing, canary deployments, autoscaling
Model performance monitoring, drift detection, SLO-driven operations
Hardware-aware cloud platforms for AI: GPU clusters, job schedulers, inference/training stacks, and aggressive optimization for performance and cost at scale.
NVIDIA GPU Operator, node pools, scheduling for training and inference
vLLM, Ollama, multi-model serving with intelligent routing
Spot instances, autoscaling, GPU utilization monitoring, right-sizing
Inference latency, queue depth, GPU metrics, training job monitoring