Mandip Adhikari

I work at the seam between AI/ML systems and the infrastructure that keeps them honest in production — data pipelines, identity, search evaluation, and the math that makes models actually scale.

Open to backend, full-stack & infrastructure rolesHattiesburg, MS

01Experience

Aquaview

Feb 2025 — Present

Software Developer & Team Lead · Institute for AI Applications — University of Southern Mississippi

Python · GCP Cloud Run · GDAL · PostgreSQL · OAuth2 / OIDC · MongoDB

Built a streaming statistical-analysis pipeline (Welford's algorithm + reservoir sampling) over 10M+ observation datasets, with complexity-score routing between sync and async Cloud Run jobs to keep cost down and previews instant.
Shipped a production OAuth2/OIDC identity provider from scratch — PKCE auth-code flow, RS256-signed ID tokens, SHA-256 hashed token storage with MongoDB TTL expiry — powering 15 endpoints and 6 scopes, plus a JavaScript SDK for one-import login.
Deployed a cloud-optimized raster tile server on Cloud Run serving 268K+ datasets in COG / STAC / Zarr formats, with tuned GDAL caching (HTTP/2 multiplexing, 200MB curl cache) and 4 custom scientific colormaps.

Illumibot

Oct 2025 — Dec 2025

Software Engineering Intern · ArroyoDev

Python · FFmpeg · Mobile APIs · Inference Services

Profiled and optimized the computer-vision segmentation pipeline for projector workloads — a 40% cut in processing overhead, turning a laggy mask-generation engine into something that runs in real time.
Built a frame-accurate video transition system on FFmpeg xfade / acrossfade filters with seamless multi-clip chaining.
Hardened the mobile ↔ inference API layer with request batching, timeouts, and retry logic — a 30% reduction in service failures and predictable end-to-end latency.