Ethann Haq

Architected and owned multi-cloud AWS and GCP infrastructure for a mental health SaaS platform with 99.97% uptime for 200k+ active users. Built and operated a Kubernetes-based microservices platform with autoscaling, reducing infrastructure spend by 32% through right-sizing and spot instance strategies. Established an SRE programme with SLOs/SLIs, error budgets, and runbooks, reducing MTTR by 60% and preventing repeat incidents via structured post-mortems. Delivered GitOps CI/CD pipelines using GitHub Actions and ArgoCD, cutting release cycles from 3 days to under 90 minutes with zero-downtime deployments. Implemented Terraform and Helm for repeatable, auditable environments and removed configuration drift. Created comprehensive observability with Prometheus, Grafana, and PagerDuty, including 150+ dashboards and automated alerting; improved MTTD by 55%. Led security hardening with least-privilege IAM, Vault-managed secrets, pod security standards, and automated compliance scanning usin

Owned and maintained highly available AWS infrastructure (EKS, EC2, RDS Aurora, S3, CloudFront) supporting AI-driven medical imaging pipelines processing 5TB+ data daily. Migrated from legacy monolithic deployments to Kubernetes microservices, improving horizontal scalability and reducing deployment times by 50%. Implemented GitOps workflows with ArgoCD and Flux CD for consistent version-controlled rollouts across dev/staging/production. Built centralized observability using Prometheus, Grafana, and ELK to enable proactive alerting and reduce P1 incidents by 42%. Automated provisioning and configuration with Terraform and Ansible, reducing provisioning time from days to 20 minutes. Led security reviews and implemented network segmentation, WAF rules, and vulnerability scanning pipelines aligned to ISO 27001 requirements. Drove FinOps initiatives such as reserved instance planning, S3 lifecycle policies, and rightsizing, reducing monthly AWS costs by 28%.

Designed and provisioned AWS infrastructure from scratch for AI/NLP services, including real-time speech-to-text APIs handling 50k+ daily requests. Developed Docker containerization strategy and deployed production workloads to EKS, standardizing dev/staging/production environments and eliminating environment drift. Implemented Jenkins and GitLab CI pipelines with automated testing gates and rollback triggers for confident zero-downtime releases of ML model serving APIs. Integrated Apache Kafka for event-driven audio transcription pipelines, improving throughput by 35% and enabling real-time processing at scale. Deployed centralized monitoring and logging with CloudWatch and Datadog, reducing time to identify issues by 50%. Implemented cost optimization via tagging governance, scheduled scaling, and EC2 reserved instances, reducing monthly AWS spend by 25%. Partnered with ML engineers to streamline model deployment workflows, reducing model-to-production lead time from 2 weeks to 3 day