Teams at AiDash

Architect and operate the MLOps infrastructure for production model deployment, scalable inference, cost optimization, monitoring, failover, and disaster recovery. Build SageMaker-based serving and CI/CD systems with automated testing, staged rollouts, elasticity, and resilience. Establish MLOps standards as the first engineer on a new team while partnering with Data Science and Web Application teams to enable rapid, reliable model releases.

Build, test, and ship backend services and APIs in Java or Python. Work on scalable data pipelines processing satellite, sensor, and operational data; write documented, tested code; participate in reviews; debug production systems; and present an end-to-end project. The internship includes mentorship, cross-team collaboration, and potential consideration for a full-time return offer.

Leads the creation of AiDASH’s Data Inference AI Pipeline team, including hiring, architecture, engineering practices, and delivery. Designs scalable, cost-efficient multimodal AI and ML inference pipelines, training and human-feedback loops, and production AI systems. Manages engineering leaders and distributed teams while partnering with Product, Data Science, Design, and executive leadership on roadmap, architecture, resourcing, and enterprise customer delivery.