AI Engineer

About the role

Build the deterministic AI execution layer for enterprise operations

> Our Thesis: While consumer AI tools and coding assistants (like Claude Code) focus on unconstrained, single-user tasks, mission-critical enterprise operations require a fundamentally different architecture. Enterprise adoption cannot rely on free-roaming personal agents. It demands deterministic execution engines, robust operational guardrails, automated verification loops, and bulletproof process orchestration.

Enterprise software integrations are still painfully fragile: workflows break silently, APIs fail unpredictably, systems drift out of sync, and teams waste countless hours maintaining infrastructure never built for real-time operations at scale.

Our platform powers operations for supply chains, logistics, healthcare, financial operations, e-commerce, and enterprise sales. When our systems run, AI agents execute business processes deterministically: orders move, invoices sync, warehouses operate, contracts renew, revenue gets recognized, and critical workflows execute in real time.

What You Will Work On in Your First 90 Days

- Deterministic Execution Engines: Build production-grade AI agents designed for structured process execution rather than open-ended chat loops.

- Guardrails & Evaluation Suites: Implement programmatic guardrails, schema enforcement, human-in-the-loop fallback triggers

This Role IS for You If

- You understand why enterprise AI requires strict guardrails, transactional guarantees, and deterministic process execution over unconstrained agents.

- You are a systems-minded full-stack engineer who loves tackling difficult backend, distributed queue, and non-deterministic agent problems.

- You think like a product owner, take end-to-end ownership, and thrive in fast-moving startup environments.

This Role IS NOT for You If

- You think building enterprise AI is just writing prompts or making simple calls to OpenAI/Anthropic APIs.

- You dislike operational complexity or debugging non-deterministic failure modes and state drift.

- You require long, slow timelines or highly structured corporate environments.

Tech Stack & Tooling

Languages & Frameworks: Python, React, Postgres

⚠️ Apply By Answering ⚠️

1. What excites you about building guardrailed, process-executing AI execution engines over open-ended chat assistants? (2–3 sentences max)

2. What is the most technically complex agent workflow, guardrail system, or real-time distributed pipeline you've built? Highlight how you handled state, failure modes, or verification loops.

About the interview

• Meet with the CTO
• Real-world technical scenarios (not LeetCode)
• System design and architecture discussions
• Production/debugging-oriented conversations

About Stacksync

Stacksync powers real-time and two-way data synchronization between CRMs and Databases. Engineers and RevOps teams use Stacksync to (1) build backbone CRM integrations, (2) build internal tools on real-time customer data and (3) ship customer metrics from their database/data warehouse to their business teams.