Vamsi Alla

Senior Software Engineer · Building production AI systems (LLM, RAG, agents)

I've built production software for 12+ years and spent the last 3+ designing, evaluating and deploying LLM and agentic AI applications for enterprise customers. I work directly with client engineers, product owners and executives to find the real problem, then own the solution end to end: architecture, data and retrieval pipelines, the AI layer, evaluation, deployment and handoff.

Open to Forward Deployed Engineer and senior AI engineering roles · Remote, USA

01

Selected impact

  • ~60% faster document processing and ~55% lower model running cost, by replacing a manual enterprise workflow with a multi-agent AI platform, architected and delivered end to end.
  • 90%+ field accuracy on structured extraction in production, by routing low-confidence fields to human review instead of letting the model guess.
  • Interactive-speed semantic search across a large job taxonomy, by tuning vector retrieval and metadata filtering in Aurora PostgreSQL with pgVector.
  • A reusable RAG layer, evaluation harness and guardrail configurations that later engagements adopted as their starting point instead of rebuilding.

From enterprise work. The roles and projects behind each item are on the Experience page.

02

Featured project

The live Hotel Operations Knowledge Assistant answering whether a guest can bring their dog at Beach Resort, with a citation and its source

Beach Resort · “A Gold member wants late checkout on Saturday. How late can they stay?”

On Saturday, a Gold member's late check-out is capped at 12:00 PM for all guests, including Gold members [1]. The brand-wide Gold benefit of 2:00 PM does not apply on weekends because the Beach Resort property rule overrides it [1] [4].

[1] Beach Resort policies › Late Check-out (Property Override) · [4] Guest Rewards Loyalty Program › Tiers and Benefits

Independent project · simulated client engagement for a fictional hotel group · synthetic data

Hotel Operations Knowledge Assistant

A RAG assistant that gives hotel front-desk staff cited, property-specific policy answers, refuses when the documents don't say, and flags when to check with a manager. New users see what it knows and get suggestions it's tested to answer.

98%
answer accuracy on the live app (54-question eval, 3 runs)
98%
strict retrieval: the right passage in the top 5
43% → 100%
first-question success on 49 questions visitors type first
275
tests, 97% coverage, eval gates in CI

About $0.17 per 1,000 questions on the live app ($0.19 with hybrid search).

03

One engagement, five stages

The hotel project runs like a real client engagement, one stage at a time, for the same fictional hotel group.

  1. 1

    Hotel Operations Knowledge Assistant

  2. 2

    Guest Request Agent

  3. 3

    Eval Harness

  4. 4

    Production Deployment

  5. 5

    Client Handoff

What each stage delivers