anyesh

Backend and product engineering on a cloud-based SaaS for home care. Architected the Support at Home reconciliation pipeline, built a third-party-API testing harness, and lead AI coding-agent adoption as the Australian engineering team's AI champion.

• Support at Home (2026) — architected and built the entire reconciliation engine and pipeline for Services Australia's new Support at Home funding model (the successor to Home Care Packages).
• Testing harness (2026) — Services Australia doesn't provide sandbox or test endpoints, so I architected and built our own: a mock / monkey-patching layer that suppresses third-party API calls in-app and lets us run the rest of the system end-to-end against fixtures.
• AI champion, Australian engineering team (2026) — proposed and built our end-to-end Cypress AI test-generation framework on top of Cursor's hooks, skills, and commands. Given a PRD or Jira ticket, the LLM explores the web app through browser tools, finds useful selectors, maintains memories and navigation indexes, and writes Cypress specs nearly one-shot.
• Engineered HCP invoicing, claiming, smart reconciliation, and budget management. 30% reduction in customer support tickets.
• Shipped the auto travel time feature in the scheduling and payroll workflow. 25% efficiency gain for clients.
• Pioneered a core product delivery team driving technical design and strategic value for Australian clients. 100% compliance with Australian standards.

ML engineer across three Fusemachines clients: TIME Magazine, Hospital for Special Surgery, and Fuse AI. Pipelines, OLAP consolidation, viral-content prediction, implant supply chain optimization, and ML curriculum.

• TIME Magazine — streamlined data pipelines for brand audits, insights, and strategic recommendations. Consolidated scattered data into a single OLAP system.
• TIME Magazine — collaborated with the Data VP and a Ph.D. on a viral-content prediction ML pipeline. 90% accuracy.
• TIME Magazine — automated reporting systems. 75% reduction in report generation time.
• Hospital for Special Surgery — ML-driven implant supply chain optimization. Analyzed 15,000+ cases, predicted patient implant needs at over 90% accuracy.
• Fuse AI — restructured and rebuilt the best ML and deep learning academic courses taught in Fuse Classroom to tens of thousands of students worldwide. Implemented core ML and NLP algorithms at the maths level to explain mechanics.

Frontend engineering on a crypto exchange platform. Built and deployed the JS frontend and backend services, plus the user authentication and trading flows.

• Front-end development of a crypto exchange platform.
• Architected the deployment of JavaScript backend and frontend services. 40% increase in deployment workflow.
• Engineered best practices for user authentication and secure trading management.