▍ Amey Parmarthi

Enterprise AI Architect · AI Deployment Leader · Forward Deployed

I take AI agents
from pilot to production
at enterprise scale.

Forward deployed at Salesforce as the technical lead on production agentic AI for Fortune 10 and Fortune 25 enterprises — I design the agent, set the guardrails and the eval bar, and lead cross-functional teams to go-live. Before that, five years building and leading a team of solution architects. I also build agents end to end on the open LLM stack, so my architecture calls hold up in code.

Fortune 10 + 25
production AI agent deployments
60–90 days
discovery to go-live
5 years
leading solution architects · 2 promoted
15 years
enterprise delivery · Deloitte → Salesforce

Experience

15 years · Deloitte → Salesforce

AI Deployment Strategist · Forward Deployed (Agentforce)

Salesforce
Nov 2025 — Present
  • Fortune 10 enterprise: technical lead on a voice-first, multi-agent customer-service system on Agentforce Voice and Data Cloud — authentication, intent routing, order and account self-service, off-topic handling, and escalation.
  • Fortune 25 enterprise: deployed production Identity AI — MFA unlock, password reset, and account management orchestrated through MuleSoft, with containment-first guardrails.
  • Design the agent intelligence and take it through design reviews and POCs to architecture sign-off; deploy pilot and beta Agentforce Voice capabilities, including private connectivity.
  • Built the eval taxonomy, UAT framework, and defect-triage logic that drive go-live. Lead tech leads, QA leads, and customer engineers without formal authority.

Senior Manager, Solutions Architecture

Salesforce
Jan 2021 — Oct 2025
  • Built and led a team of 5–6 Solutions Architects and Senior SAs delivering enterprise AI and CRM programs for Fortune 500 customers. Owned hiring, performance, and promotion cases; promoted 2 to Senior SA.
  • Owned capacity planning across concurrent engagements and codified the delivery playbooks that made ad-hoc engagements a repeatable function.
  • Player-coach on early Agentforce pilots — prompt patterns, safety guardrails, success criteria — and on C-suite roadmaps tied to KPIs and value realization.

Solutions Architect

Salesforce
Jan 2015 — Dec 2020
  • Architected and delivered enterprise implementations for Fortune 500 technology customers including Google, Cisco, Dell, EMC, Fitbit, and NetApp.

Senior Consultant — Salesforce Program Lead

Deloitte Consulting
2011 — 2015
  • Led enterprise Salesforce programs for Fortune 500 clients from architecture and integration design through go-live.
Education
  • M.S. Computer Science & EngineeringUniversity at Buffalo (SUNY)
  • AI & Machine LearningMIT · 2022–23
  • AI/ML Engineer Career TrackSpringboard · 2026
  • Product & Agile ManagementUC Berkeley Executive Education
Certifications
Agentforce SpecialistData Cloud ConsultantApplication ArchitectPMPPSM IPSPO I
Status

U.S. Permanent ResidentNo sponsorship required

What I've built

hands-on · measured, not projected
01live

FinSight — Financial Evidence Engine

hybrid RAG · cited answers · refuses when evidence is thin

Answers analyst questions over 15K earnings-call chunks with inline citations, an evidence panel and a pipeline trace, and refuses when the corpus can't support an answer. Hybrid retrieval + reranking beats dense-only by 27% NDCG@10. It's live: a React app on Vercel streaming from a FastAPI service on Cloud Run, with telemetry in Postgres and a behavioral eval you can run from the browser.

+27%NDCG@10 vs dense-only · RAGAS faithfulness 0.81 · $0.014/query live
LangGraphClaudeQdrantVoyageCohere RerankFastAPIReactPostgresCloud Run
FinSight Ask page: a grounded answer about Apple's 2020 iPhone supply chain with inline [N] citations and a ranked evidence panel
02live

Churn Decision Intelligence

production ML · decision policy · ROI

Churn prediction on KKBox's ~33 GB subscription dataset, turned into a decision: who to contact, through which channel, and what it nets. The naive "call the top 10K" campaign loses $6.8K per cycle; the ROI-optimized policy nets +$17.7K and a tiered policy +$24.3K. Served from FastAPI on Cloud Run with per-score SHAP, behind a live decision app, with evals that gate every deploy.

+$24.3Ksimulated net value per cycle, tiered policy · vs −$6.8K naive top-10K
DuckDBLightGBMFLAMLMLflowSHAPFastAPIReactCloud RunGitHub Actions
Churn Decision Intelligence overview: the 0.76% of subscribers who drive 43% of churn, with KPI tiles
03live

Tenant-Safe Support Agent

multi-tenant agent · human-in-the-loop · eval-driven architecture

A customer-support agent with six tools, human approval on every refund, and tenant isolation enforced at the server before any conversation state loads. I pre-registered pass bars, then benchmarked a flat agent against a supervisor: both resolved 30/30 with zero security failures, flat was 1.7× faster with fewer tool calls — so flat shipped. It's live: sign in as one of two customers and try to read the other's account. 163 deterministic tests in CI, 8 code evaluators plus an LLM judge, 25 ADRs.

30/30resolved · 0 security failures · p50 3.8s vs 6.6s supervisor
LangChainLangGraphLangSmithOpenAIFastAPINext.jsSQLitepytest
Live app, signed in as Helena: a prompt-injection request for another customer's invoices is refused with no tools called
04live

Member Service Agent

chat + voice · one graph · safety before the model

A member-service agent built on one rule: the LLM decides intent; code decides truth. A deterministic crisis gate runs before any model call, step-up authentication is a graph node rather than a prompt instruction, and order status and refunds come from config-driven engines. FastAPI + Postgres backend with checkpointed sessions and a full turn audit, and a Next.js operator console that shows every routing decision. On a live model, held-out crisis recall is 1.00 with Claude Haiku 5.5, the cheapest model that passes every gate. It's live: pick a synthetic member, ask for a refund, and approve it yourself as the specialist, in plain text.

1.00held-out crisis recall, live · keyword gate alone 0.13
LangGraphFastAPIPostgresNext.jsClaudePydanticpytest
1.00
held-out crisis recall, live · 0.13 keyword gate alone
2.3s
p95 turn on Claude Haiku 5.5, live · 1.1s p50
1.00
routing accuracy and task completion, live
25/25
injection attacks blocked, mock and live
$0.052
per 1,000 turns on Haiku 5.5 · 101 tests
05open source

Nimbus Account Assistant

native Agentforce · confirmation-gated writes · deterministic data

An account-service agent built natively in Agentforce Agent Script: a router, seven subagents (four doing work, three guardrails) and five Apex and Flow actions. Writes stay locked until a read-only preview fills the gate variables; record IDs pass through filter_from_agent so the model never sees them; money and outage figures come only from Apex or Flow. Deliberately no RAG — the data is structured.

7subagents · 3 of them guardrails · 5 Apex/Flow actions
AgentforceAgent ScriptApexFlowSalesforce CLI
7
subagents, 3 of them guardrails
5
actions: 1 Apex, 4 Flows
0
figures computed by the model

How I deploy AI

architecture · evals · leadership
01

Architect for production

Enterprise agents fail where demos don't look: authentication, escalation, edge cases, sensitive data. I design those paths deterministically and keep the model away from anything that has to be true — money, IDs, account state — so the system clears security review.

02

Make quality measurable

Go-live is an eval decision. I define the failure taxonomy, set pass thresholds before the run, and separate agent failures from integration, data, and prompt failures — then let the numbers choose the architecture, and correct any claim that doesn't hold up live.

03

Lead without authority

Fifteen years between executives and engineers. I run design reviews, align customer, product, and engineering teams on one plan, and hold 60–90-day timelines — and because I build agents myself, my architecture calls hold up in code.