AI-Powered Pre-Sales Engineer — Forward Deployed

IFS; Palo Alto (CA); On-site; USD 190,000 - 230,000; Full time

Benefits offered by this job

Salary range $190k–$230k OTE annually

Flexible paid time off

401K with company contribution

Flexible spending accounts

Life insurance and disability benefits

Tuition assistance

Community involvement and volunteering

Job summary

IFS Loops is seeking a Pre-Sales Forward Deployed Engineer in Palo Alto to join a founding team blending presales and product. You will run discovery with prospects, define agentic use cases, and build PoCs using the IFS Loops platform, delivering value fast.

You will present PoCs to technical architects and business leaders, work with Account Executives and engineering, and help shape the roadmap with field learnings and scalable patterns.

Qualifications

• Track record in presales, solutions engineering, or forward‑deployed roles.
• Experience with enterprise AI, LLMs and multi‑agent design.
• Willingness to travel 25–50% to customer sites.

Responsibilities

• Own the full arc from conversation to proof: run discovery with prospects and customers, identify agentic use cases and build PoCs.
• Present PoCs to both technical architects and CxOs, iterating from demos to business value.
• Partner with Account Executives and Loops product/engineering to turn patterns into platform capabilities.
• Shape the Loops roadmap from field learnings, prioritizing what Customers actually ask for.
• Train presales on platform capabilities and run global workshops.

Skills

Presales

Solutions engineering

Forward‑deployed

Product instincts

AI/agentic AI

Tools

Agent Studio

LLMs

Job description

IFS Loops is seeking a Pre-Sales Forward Deployed Engineer in Palo Alto to join a founding team blending presales and product. You will run discovery with prospects, define agentic use cases, and build PoCs using the IFS Loops platform, delivering value fast.

You will present PoCs to technical architects and business leaders, work with Account Executives and engineering, and help shape the roadmap with field learnings and scalable patterns.