Software Engineer
viknesh = {
"role": "Software Engineer",
"building": "AI applications and the systems underneath them",
"degree": "B.Tech, Artificial Intelligence & Data Science",
"philosophy": "no toy demos — if it isn't deployed, it isn't done",
"open_to": ["engineering roles", "open-source collaboration"],
}I write backend services and machine learning code, and I'm most interested in where the two meet — taking a model that works in a notebook and turning it into something that holds up when real people use it. Everything on this profile is personal work, built to learn something specific rather than to fill a portfolio.
Backend engineering — APIs, services, data models, and the boring reliability work that decides whether any of it survives contact with users.
Machine learning — training, evaluation, and knowing when a model is genuinely better versus when the metric just moved.
AI applications — LLM-backed features end to end: retrieval, orchestration, guardrails, and serving.
Data — pipelines, quality, and the unglamorous cleaning that determines everything downstream.
The squares are sparse today. That's the point of a graph that keeps going —
it's a record of a habit, not a scoreboard. Check back in a year.
