# Lead Systems Architect: Enterprise AI Voice & API Integration (Stealth Venture)

**URL:** https://www.upwork.com/jobs/~022023992572648299009
**Submitted:** 2026-02-18 ~12:43 AM EST
**Rate:** $65/hr ($58.50 after fee)
**Rate Increase:** Never
**Connects:** 20 spent (55 remaining)
**Score:** Hot Lead (80+)

## Client
- **Rating:** 4.94 (24 reviews)
- **Spent:** $31K
- **Location:** Stamford, CT, USA
- **Member since:** Nov 2014
- **194 jobs posted**, 23% hire rate, 9 active hires
- **Avg hourly rate paid:** $23.79/hr (⚠️ low avg but posting goes to $70)
- **Payment verified**

## Job Details
- **Duration:** 3-6 months, 30+ hr/wk
- **Level:** Expert
- **Range:** $15-$70/hr
- **Contract-to-hire opportunity**
- **Skills:** Twilio API, API, CRM
- **Competition:** 15-20 proposals, 0 interviewing

## Cover Letter

SYSTEMS

I build the kind of infrastructure you're describing — voice-to-API pipelines, self-healing workflows, and CRM orchestration engines that handle high-volume transactional data without breaking silently. This isn't a side skill for me; it's what I do full-time.

Most Complex State-Managed Automation:
I built an enterprise communication engine that orchestrates real-time voice calls (Twilio/Bland), SMS sequences, and email campaigns triggered by CRM pipeline events. The system manages a finite state machine per contact — tracking call disposition, follow-up cadence, opt-out status, and escalation triggers across channels. Each state transition writes to a centralized log (Supabase) with full audit trail, and the orchestration layer (Make.com + custom webhooks) handles branching logic: if a voice call fails, it falls back to SMS with an LLM-personalized message, then escalates to a human agent if no response within the configured window. The system processes 500+ contacts/day with zero silent failures — every error routes to a dead-letter queue with automatic retry logic and Slack alerting.

API Rate Limit Strategy:
For high-volume transactional processing, I implement a three-tier approach:
1. Token bucket rate limiting at the orchestration layer — requests are queued and released at the API's documented rate ceiling
2. Exponential backoff with jitter on 429 responses — prevents thundering herd when limits are hit
3. Circuit breaker pattern — if an API endpoint fails 3x consecutively, the circuit opens and routes to a fallback path (cached data or delayed queue) rather than hammering a degraded service

For this project specifically, I'd implement per-endpoint rate tracking in Supabase with a sliding window counter, so the orchestration layer knows exactly how much headroom remains before hitting limits.

What I bring to this role:
- Deep Twilio/Bland/Vapi experience for low-latency voice with real-time function calling
- Production Make.com workflows with proper error routing, not just happy-path automations
- Advanced prompt engineering for GPT-4o and Claude — I build prompt chains that handle edge cases, not just demos
- OAuth2 API integration across CRM platforms (HubSpot, ServiceTitan, Salesforce)
- Self-healing architecture as a default, not an afterthought

I'm US-based (EST), available 30+ hrs/week immediately, and I understand that "hardened infrastructure" means the system works at 3 AM when nobody is watching.

Portfolio: https://portfolio.mcpengage.com