Teams at Fetcherr

Leads a team of MLOps engineers building and operating machine learning infrastructure, data pipelines, and model-lifecycle systems. Owns initiatives from design through production, mentors engineers, establishes technical standards, and partners with R&D to deliver scalable research and production workflows. The role involves hands-on coding, distributed computing, pipeline orchestration, cloud platforms, Kubernetes, Docker, and migration from Dask to Ray.

Lead and mentor an MLOps engineering team while owning the design, development, and production operation of machine learning infrastructure and pipelines. Drive model-lifecycle initiatives, ensure reliable training and inference data, partner with R&D stakeholders, contribute hands-on code and technical direction, and help migrate distributed workloads from Dask to Ray.

Lead the transformation of infrastructure into a scalable, Kubernetes-native self-service platform. Build and grow the Platform Engineering team, establish infrastructure as code and GitOps practices, improve observability and reliability, modularize architecture, and enable secure, autonomous development workflows. The role requires hands-on cloud-native engineering across GCP services, Kubernetes, Terraform, Helm, and automation, along with mentoring, hiring, operational excellence, and blameless incident learning.