Felix Falkenberg

Architected and operationalized scalable ML pipelines, deploying production-grade ML services and end-to-end data processing pipelines. Led Kubernetes and Docker-based deployments, implemented infra-as-code, and built monitoring solutions. Worked across microservices, data platforms, and ML systems; familiar with OpenShift, ArgoCD, Terraform, Airflow, and GitOps. Used AWS (EKS, S3, Lambda, CloudWatch, IAM) for cloud-native workloads. Supported project management and requirements engineering for machine learning use cases.

Implemented and operationalized scalable services and infrastructure across microservices and data platforms. Ensured production-readiness through infrastructure-as-code, automated pipelines, and comprehensive monitoring. Orchestrated deployment, scaling, and maintenance of ML microservices using Kubernetes and Docker in production. Proficient in OpenShift, Kubernetes, ArgoCD, Terraform, Airflow, and GitOps methodologies. Deployed and managed cloud-native infrastructure and workloads on AWS (EKS, S3, Lambda, CloudWatch, IAM). Designed robust, scalable data pipelines for data processing and analysis. Led project management and requirements engineering for ML use cases.

Architected and deployed ML pipelines from prototyping through production implementation; advanced ML research using healthcare data; translated complex research questions into actionable statistical models; presented results with visuals; applied deep learning frameworks in Python and TensorFlow; implemented scalable services and infrastructure across microservices and data platforms; ensured production readiness via IaC, automated pipelines, and monitoring; managed ML microservices using Kubernetes and Docker; proficient with OpenShift, Kubernetes, ArgoCD, Terraform, Airflow, and GitOps; deployed cloud-native infrastructure on AWS (EKS, S3, Lambda, CloudWatch, IAM); designed scalable data pipelines; conducted project management for ML use cases.