Teams at Fusemachines

Designs and operates production machine learning systems on Azure, including training, deployment, monitoring, retraining, governance, and retirement workflows. Productionizes data science prototypes into deployable Python services, builds CI/CD pipelines, manages MLflow model lifecycles, develops feature engineering pipelines, and operates batch, streaming, and real-time inference. The role also establishes testing, observability, security, lineage, access controls, platform standards, and cost-efficient ML infrastructure.

Convert data‑science prototypes into production ML services; build and operate Databricks/Spark pipelines reading/writing Snowflake; manage full model lifecycle (MLflow, CI/CD, retraining, drift monitoring); deliver/version audience segments to ad‑tech partners; ensure scalability, cost efficiency, privacy, and mentor engineers.

Administer and maintain RHEL and OpenShift clusters on bare-metal, manage node lifecycle and hardware (Dell), apply patches/upgrades, monitor cluster and hardware health, support IBM Watson/Cloud Pak deployments, manage storage and backups, implement security hardening, coordinate vendor and colocation support, document runbooks and perform incident response.