Vetted Blue-Green Deployment Professionals

“Longtime early-stage startup operator (since 1998) now running a solo consulting practice focused on SaaS infrastructure and platform strategy. Former angel investor in a medical information SaaS company that went public, with a strong focus on making prototype/demo systems enterprise-ready through efficiency, reliability, and performance improvements, and a nuanced view of how AI/LLMs may change engineering team models.”

“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”

“IBM Power/AIX infrastructure engineer who has owned large-scale Power9/AIX 7.x estates across primary/DR (VIOS/HMC/vHMC) and has deep hands-on experience with DLPAR, shared processor pool governance, and PowerHA recovery. Also brings modern DevOps/IaC capability—built Azure DevOps/GitHub CI/CD for Terraform + Kubernetes/Helm with strong security controls and resolved a production Terraform state-lock failure by redesigning backend locking and pipeline concurrency.”

“Backend/data engineer with Amazon supply-chain experience building production serverless Python services and ETL pipelines on AWS (Lambda, API Gateway, S3, RDS, Glue). Has modernized legacy SAS jobs into Python with rigorous parity testing and phased migrations, and has delivered major SQL performance gains (minutes down to seconds) through indexing and partitioning.”

“DevOps/SRE focused on cloud-based distributed systems, with strong hands-on Kubernetes production experience (microservices deployments, Helm, probes, resource tuning, CI/CD and Docker build standardization). Demonstrated end-to-end troubleshooting across application, infrastructure, and networking layers—e.g., isolating degraded storage via node disk I/O metrics and restoring performance by draining the node and replacing the volume. Builds Python automation for operational reliability, including scheduled Kubernetes secrets rotation integrated with an external secret manager.”