Signals
hackathon → productionizing
company-wide, Walmart Global Tech
vs. the old Slack-and-wait cycle
async Cassandra fan-out
registry-size-dependent → payload-only, Direct Feast Apply
Experience
Self-initiated AI work leads; Feature Store platform ownership underneath all of it.
Self-Initiated
Akira — Autonomous Multi-Repo Coding Agent
Not a general-purpose coding assistant — purpose-built to operate across multiple repositories end-to-end. Reads a Jira ticket directly, plans the implementation, and ships production-ready code with no manual trigger. Model-agnostic: works with Claude, Codex, or internal tools like Code Puppy and Wibey. Never sprint-planned — started as a hackathon build and is now on the path to company-wide productionization, a category of tool few organizations outside frontier AI labs have shipped internally.
Self-Initiated
multi-agent-driven-dev — Plugin, Top 3 Company-Wide
Repackaged Akira's multi-agent architecture into an installable plugin any team could adopt directly, without needing a standalone deployment — now ranks among the Top 3 most-downloaded internal developer plugins across Walmart Global Tech.
Self-Initiated
efs-onboarding — Self-Service Onboarding Plugin
Feature Store onboarding is a complex, multi-step process that documentation alone couldn't cover — platform engineers were repeatedly pulled off roadmap work to hand-hold the same steps. Built, unprompted, a full self-service plugin: walks new teams through setup, configures the streaming pipeline, and helps debug pipeline code errors. Now used across the entire EFS user base, cutting response time an estimated ~75% vs. the old Slack-and-wait cycle.
Platform Modernization
Async Online Feature Retrieval
Cut multi-feature online lookup latency by an estimated ~67-80%: replaced blocking, synchronous Cassandra reads with asyncio-native concurrent fan-out, validated end-to-end against live data before shipping.
Feature Store Core
Direct Feast Apply — Registry-Size-Independent Registration
Architected "Direct Feast Apply": cut per-run registration cost from ~728ms/existing feature view to ~70ms/payload — registry-size-independent, a gap that widens as it grows.
Feature Store Core
"Promote to Prod" Feature-Promotion Architecture
Owned the end-to-end design of the feature-promotion architecture across environments — solving the core complexity of provisioning per-environment feature instances while maximizing reuse and eliminating duplication between nonprod and prod.
Feature Store Core
UPS Online Store Integration
Architected EFS's integration with UPS, a new online-store type neither EFS nor Feast supported — went deep into Feast's open-source internals to design and ship the integration end-to-end, deployed through stage and production, opening a new consumer-facing serving path for the platform.
Platform Modernization
SDK Release Pipeline & GCP Vertex AI
Migrated 2 SDK release pipelines off a legacy, manually-operated process onto a governed KITT pipeline with branch-gated semantic versioning, and integrated GCP Vertex AI (MLOps) into the feature registry — meaningfully cutting per-release manual effort and closing a version-mismatch bug class behind 3 documented production incidents.
Earlier, built a 190-PR full-stack platform (Target Ally) before joining the EFS team.
Open Source & Personal Projects
Outside the day job, on my own time — self-hosted infrastructure and upstream contributions to projects I don't own.
Personal Project
podcast_summarizer — Self-Hosted Media-AI Pipeline
A self-hosted pipeline condensing news articles and YouTube videos alike — a recursive-chunking map-reduce engine works around single-call LLM context limits, narrated through a pluggable multi-backend TTS layer.
Open Source
Feast — 3 Merged Pull Requests
Upstream contributions to the same open-source feature store EFS's Python SDK is built on.
Most notable: fixed a silent stale-data bug where a feature view and a stream feature view
sharing a name caused get_online_features to silently
return outdated data from the wrong registry table — a wrong-answer bug, not a crash.
Also shipped stream-source config support and a Pydantic-version compatibility fix.
Open Source
OpenJarvis — 4 Merged Pull Requests
Fixed multiple silent failure modes in this open-source AI agent framework: session history silently dropped between channel messages (the agent "forgot" every prior turn), and a Telegram delivery crash whenever a reply exceeded the platform's 4096-character limit — both reproduced, fixed, and covered with new regression tests before merge.
Open Source
Code Puppy — 2 Merged Pull Requests
Found and fixed a bug in this open-source AI coding agent's retry-backoff logic that inverted its own documented behavior on a progress reset, plus a crash on single-attempt retries — merged same day, backed by 2 new regression tests. Also shipped opt-in project-scoped session filtering — saved conversation sessions can now be filtered to the current working directory across the CLI, slash command, and interactive pickers, with existing unfiltered behavior untouched and 22 new tests passing.