Vetted Amazon CloudWatch Professionals

“Senior infrastructure/platform engineer with deep IBM Power/AIX (Power9, VIOS, HMC, LPAR/DLPAR) and PowerHA production ownership at scale (40 frames / ~300 LPARs), including hands-on outage recovery and performance tuning. Also delivers modern DevOps/IaC capabilities—CI/CD for Kubernetes microservices and Terraform-based multi-account AWS (EKS/VPC/IAM/RDS) with drift detection and safe rollout controls.”

Brian Sanders

Screened

Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines

Chicago, IL12y exp

“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”

Bocheng Wan

Screened

Mid-level Software Engineer specializing in consumer robotics and machine learning

Sunnyvale, CA4y exp

“Developer with a structured AI-assisted coding workflow centered on algorithmic rigor, type-safe scaffolding, and spec-driven modular design. Used Claude Code to help build an Android SDK for IPC, quickly ramping up on AIDL and compressing delivery including unit tests from several weeks to two weeks.”

“AI-native engineer who says 100% of current development is done through coding agents and reports roughly 10x productivity gains. Currently building 'AI TV,' a production video-generation system with multi-agent orchestration, including a motion-graphics pipeline where agents handle high-level design, low-level implementation, and iterative review. Particularly strong in designing codebases and workflows so AI systems can effectively use architectural and product context.”

“Engineering leader who built and delivered an IoT smart-spaces platform for the self-storage and smart-living domains, translating customer requirements into architecture, capability maps, and a multi-milestone roadmap. Personally stood up missing AI/ML capabilities (including churn prediction) using Databricks (Delta Lake/MLflow), enabling follow-on features like energy optimization and security/anomaly detection. Scaled an org from 20 to 80+ with disciplined Agile planning (Jira Advanced Roadmaps/Confluence) and strong executive/customer-facing leadership during high-stakes customer commitments.”