Aditya Karnam
Hi, I'm Aditya Karnam
Senior AI Systems Engineer | LLM / Agent Platforms | Research‑Driven Tools for High‑Impact Teams
- ▸Building AI‑first systems for research‑driven and product teams.
- ▸Focus on agents, reasoning, and tooling that reduce manual work and improve robustness.
I design and ship production-grade AI systems — agent pipelines, LLM tooling, and memory layers — that teams use to move faster and with greater confidence. My work sits at the junction of software engineering rigor and applied ML research, with a focus on systems that are interpretable, maintainable, and actually useful in production.
Most agent systems are brittle at retrieval: each vector backend has its own API, and switching costs are high. embenx solves this with a unified embedding retrieval layer — a single API across 15+ vector backends with MCP support for Claude and autonomous agents. Teams replacing scattered retrieval code with embenx typically eliminate hundreds of lines of glue code and a full class of integration bugs.
pip install embenx
Claude Code's built-in skills don't cover creative or interactive generation workflows. This library adds those: a growing set of composable skills for generative art, interactive experiences, and creative tooling. Reduces from-scratch prompting time for teams building AI-driven creative features.
A modern, high-performance LMS built for speed and simplicity. Leverages a Cloudflare-native stack (Pages, D1, Workers, React Router v7) — edge-hosted courses with real-time progress tracking, automated certificate generation, and Google OAuth. Built lean so learners get fast and teams can ship without infrastructure overhead.
Cloudflare · React Router v7 · D1 · Drizzle ORMAI Toolkit
Prompt engineering without structure is guesswork. This toolkit provides interactive composers, reasoning toggles, and graders that turn ad-hoc prompting into a repeatable, auditable process — reducing iteration cycles for teams building with LLMs.
Intelligent Systems
Agent pipelines and retrieval systems designed for real workloads — robust to edge cases, observable at runtime, and built to be maintained by a team, not just their original author.
Currently exploring: Structured reasoning traces and evaluation frameworks that let teams audit agent behavior without slowing down production systems.
Building something interesting? Let's connect — I'm always happy to talk agents, LLM systems design, or research-adjacent engineering challenges.