Daniel P.

At Celtiberian Solutions, I lead the modernization and standardization of the company’s cloud and Kubernetes platform as a Senior DevOps / Cloud Infrastructure Engineer. I architected and executed the migration to a secure AWS multi-account environment using Organizations and Control Tower, establishing centralized governance, cost management, and unified security baselines. I also designed and implemented a multi-environment Kubernetes platform (EKS + hybrid) with automated TLS/PKI through Vault and cert-manager, enabling consistent, compliant, and scalable service deployments.

To strengthen reliability and operational insight, I built a centralized observability stack using Prometheus, Loki, Grafana, and Mimir, improving MTTR and enabling SLO-driven monitoring. I standardized infrastructure provisioning using Terraform and GitOps workflows, reducing manual operations and improving delivery consistency. Additionally, I optimized CI/CD pipelines across GitHub Actions, achieving a 40% reduction in deployment times and accelerating the feedback loop for all engineering teams.

At DSpot, I served as Tech Lead and DevOps Specialist, driving the cloud and platform engineering strategy for a global retail SaaS product. I architected and maintained the AWS infrastructure using Terraform and OpenTofu, enabling secure, scalable, and cost-efficient operations. I led a full migration from on-premise systems to AWS, standardizing infrastructure provisioning and significantly reducing operational overhead. I re-engineered CI/CD pipelines in GitLab, cutting deployment time by 60% and enabling reliable, frequent releases.

I also developed and maintained APIs integrating Shopify, Magento, and WooCommerce with backend systems, and delivered a secure ePOS integration that improved transaction performance by 25%. Beyond implementation, I established cloud architecture patterns, observability practices, and high-availability standards that strengthened the platform’s reliability and long-term scalability.

At datwit, I worked as an MLOps Specialist, architecting and optimizing machine learning infrastructure on AWS for large-scale, low-latency production deployments. I designed GPU-ready environments using SageMaker and ECS, enabling efficient model training and high-performance inference. I developed anonymization APIs and contributed to NER and text-similarity pipelines, improving data processing efficiency by 20%.

I implemented robust monitoring with CloudWatch, Prometheus, and Grafana to ensure reliability, proactive alerting, and faster incident response. I also introduced infrastructure-as-code practices and blue-green deployment strategies to support rapid model iteration, higher release confidence, and reproducible environments. My work helped the team scale ML workloads, reduce operational friction, and achieve more predictable and resilient production deployments.