Embedded AI Engineer for Enterprise (Client-Facing)

Varick; United States; On-site; USD 120,000 - 170,000; Full time

Job summary

Varick is hiring Forward Deployed Engineers to work directly inside large enterprise organizations. The role focuses on solving difficult business problems with applied AI and engineering.

Engineers will redesign workflows and operating models so client organizations can become more AI-native. Assignments are expected to change approximately every 3–6 months, bringing exposure to different companies and problems.

Qualifications

• Candidates must have strong software engineering capability.
• You should be comfortable writing code and designing systems.
• Able to estimate engineering effort, timelines and resources.
• Strong consulting and client-facing skills are required.
• Candidates should communicate ideas clearly and present effectively.
• Role requires confidence interacting with C-suite executives at Fortune 100 companies.
• Looking for highly driven candidates for a lean applied-AI team.

Responsibilities

• Embed closely with major enterprise clients and understand their most important operational challenges.
• Design technical systems and practical AI-enabled solutions around those problems.
• Write production-quality code as part of solution delivery.
• Help reshape business processes rather than limiting work to isolated technical features.
• Estimate project timelines and required resources with accuracy.
• Move between different client environments and problem areas as deployments change.

Skills

Strong software engineering capability

Code writing and system design

Estimate engineering effort/timelines

Consulting and client-facing

Clear communication and presentation

Interact with Fortune 100 C-suite

Lean applied-AI team mindset

Job description

Varick is hiring Forward Deployed Engineers to work directly inside large enterprise organizations. The role focuses on solving difficult business problems with applied AI and engineering.

Engineers will redesign workflows and operating models so client organizations can become more AI-native. Assignments are expected to change approximately every 3–6 months, bringing exposure to different companies and problems.