0→1 agent infrastructure · 1→100 production hardening
Shreyaan Seth · Engineering Lead
I work where an AI demo becomes an operating system: execution state, tools, memory, retrieval, evals, migrations, observability, and the backend paths that have to recover when providers do not.
Runtime thinking
The real engineering starts around it: state, boundaries, retries, costs, tool side effects, and enough history to explain what happened after the user closes the tab.
agent_runtime / run_01J7
LIVEThe job has an identity, an owner, and a recoverable starting point before execution begins.
Engineering dossier
Anonymized by design. Specific enough to show the decisions, constraints, and ownership that sat behind the code.
Built the core harness
A modular, stateless runtime around streaming model execution—designed for provider failures, runaway loops, large tool results, and agent work that rarely fits into one request.
Designed and implemented the v2 runtime
Typed workflow nodes and variables with persisted waits, approval checkpoints, duplicate-safe resume claims, failure-aware AI transforms, and per-step execution history.
Implemented the integration and scope layer
Durable retention and recall across tenant, user, external subject, agent, and knowledge-base boundaries—with rolling compaction for conversations that outgrow a model window.
Built the reusable evaluation system
Golden and adversarial datasets, deterministic tool mocks, retrieval fixtures, and structured or model-judged assertions for the places agent regressions usually hide.
Delivered the feature end to end
Kept business metrics deterministic while surrounding model output with provenance checks, critic loops, confidence signals, evidence inspection, and actionable enterprise UX.
Designed the staged migration
Separated raw provider capture from application transforms, then added incremental cursors, backfills, reconciliation tests, and explicit token and callback boundaries across Python and TypeScript.
Implemented and hardened critical paths
Built resilient browser recording with local session state, chunked upload queues, retries, and live transcription; hardened a telephony-to-realtime-AI bridge around server-side speech detection.
Operating principles
Logs, traces, costs, and run history are how a system explains itself when the demo ends.
Durable workflows get simpler when progress, failure, and ownership are visible by design.
Retries, cancellation, recovery, and isolation shape the experience as much as the happy path.
I like small boundaries, direct control flow, and infrastructure a tired engineer can reason about.
Experience
Engineering Lead · Backend & AI Infrastructure
Building major product surfaces from React and Next.js through agent runtimes, retrieval, real-time workflows, enterprise boundaries, and production infrastructure.
Software Engineer
Cut a critical API path from roughly seven seconds to 400 milliseconds, improved Next.js performance, and moved delivery to self-hosted infrastructure.
Software Developer
Built a consumer-safety browser extension that identifies suspicious websites and explains the warning instead of silently blocking the user.
Quick answers
Direct answers for founders, engineering leaders, recruiters, and the systems they use to find technical talent.
Currently open to the right problem
I'm interested in backend, AI infrastructure, platform, full-stack product, and early engineering roles where ownership is real and reliability matters.
shreyaans20@gmail.com