Founding AI Engineer — Agentic Systems & Automation (Remote)

Hidden Jobs; United States; Remote; USD 100,000 - 140,000; Full time

Benefits offered by this job

Remote-first model

Health/dental/vision coverage

Employee stock purchase program

Wellness days and flexible time off

Job summary

Hidden Jobs is forming a founding team to build agentic AI systems that automate manual work and surface risks for leaders. The role will shape architecture, workflows, and culture from day one, across chat, phone, and email for SMB through enterprise customers with internal and partner teams in four countries.

As a founding engineer, you will own system design, memory management, and context engineering, building reliable AI orchestrations that scale while keeping outputs trustworthy.

Qualifications

• 5+ years building and deploying production systems with Python.
• 2+ years working with LLMs in production, including prompt config and tool use.
• Experience with multi-step workflows, memory management, and error recovery.

Responsibilities

• Automate operational workflows and deploy automations at scale across platforms.
• Flag stalled handoffs, aged cases, SLA breaches, and review bot transcripts.
• Deliver automated BI on site health, volume, SLA, and escalation patterns.
• Build memory architecture to retain context across interactions and actions.
• Connect orchestration layers and connectors to automate end-to-end workflows.
• Measure reliability and design escalation patterns for human intervention.

Skills

Python

LLMs in production

SQL

Prompt engineering

Context engineering

Memory management

Orchestration layers

Job description

Hidden Jobs is forming a founding team to build agentic AI systems that automate manual work and surface risks for leaders. The role will shape architecture, workflows, and culture from day one, across chat, phone, and email for SMB through enterprise customers with internal and partner teams in four countries.

As a founding engineer, you will own system design, memory management, and context engineering, building reliable AI orchestrations that scale while keeping outputs trustworthy.