Applied AI Engineer

About the role
As a Forward Deployed Engineer , you’ll work on building and evolving
production-grade Voice AI systems that customers actively use.

You’ll operate close to real deployments - identifying gaps surfaced by live usage, designing
solutions, and shipping them into production. Some work moves fast; some work is planned and
iterative. What matters is that priorities are driven by real customer outcomes, not by work
defined quarters in advance.

You’ll leverage the Squad Stack platform to configure, extend, and build AI-native workflows,
while writing production-quality code where needed. You’ll work in a small pod alongside
Customer Success and AI PM partners, owning technical execution end-to-end.

This role sits at the intersection of engineering, product, and customer reality - with a strong
emphasis on code quality, system reliability, and long-term leverage over one-off fixes.

Apply even if you’re only 70% sure. The conversation itself is valuable.
What you'll do
You’ll work in a small pod (CSM / AI PM handles customer communication; you handle technical
execution), solving problems that block customer success but aren’t yet on the product
roadmap.

Your engineering work includes:

• Building custom integrations, tools, and AI workflows that unblock customer needs

• Designing and refining AI prompts, RAG systems, and LLM-based solutions

• Writing production-quality code (Python / JavaScript) with the same standards as core
engineering

• Deciding whether solutions should remain custom, become reusable, or move into the
core product

• Collaborating with core engineering teams to upstream learnings and reduce future
friction.
What you will not do:

• Run customer calls

• Own accounts

• Chase stakeholders

• Do repetitive support work
This is NOT solutions engineering
You do not deliver one-off scripts and move on
Every solution is evaluated on leverage:

• Can this be reused?

• Can this become a product primitive?

• Can this eliminate future manual work?
Custom work is acceptable only if the business makes sense, or it is experimental, where we’d
learn something new.
Strong solutions frequently get up streamed into:

• Core platform capabilities

• Reusable internal tooling

• Product roadmap items owned by core engineering