Senior Engineer · AI Systems & Backend Platform

Onkesh Bansal

I build the infrastructure that makes coding agents reliable on real codebases. That means memory layers you can audit, review gates the agent cannot skip, and a 70% cut in token cost. Ten years of production backend engineering sits underneath it.

Projects

workPerformance Engineering

Search Campaign Builder: Performance Engineering

A Java and Spring Boot service that handles Google Ads campaign data on a production ad-tech platform. I found and fixed four root causes: missing indexes, N+1 queries, lock contention, and a concurrency bug. The service then absorbed 4× workload growth at flat p99 latency.

workRuby on Rails

Media Activation: Delivery & Changelog

A changelog system I built from scratch for delivery publishing on a production ad-tech platform. It diffs before and after snapshots, calculates changes field by field, and categorizes them on the server. It now records 500 events a day across more than 5 million rows for over 100 internal users.

workAI Systems

Agent Development Harness

A six-phase development pipeline for AI coding agents, with a gate between every phase. Each gate answers a failure that kept recurring in my own PR reviews. It carries a three-tier audited memory layer and a custom Bitbucket MCP server. Search runs on ast-grep and LSP, which cuts tokens by 70%. A reviewer returns its own go or no-go verdict. I use it on my own engineering work.

personalAI Systems

CanvestAI

ETF research for Canadian retail investors, rebuilt as two systems over one SQLite database. A nightly pipeline scores 16 equity sectors through eight weighted lenses, then deliberates over the result. A LangGraph advisor answers questions by fanning out to four discipline agents per turn. Every recommendation is graded again once its horizon elapses, and each lens is graded on its own, so the weights have to earn themselves.

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Writing

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Open Source

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