About the role:

As a Forward Deployed Engineer at SquadStack.ai, youll work on building and evolving production-grade Voice AI systems that customers actively use.

Youll 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.

Youll leverage the SquadStack platform to configure, extend, and build AI-native workflows, while writing production-quality code where needed.

Youll 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.

What you'll do:

Youll work in a small pod (CSM / AI PM handles customer communication; you handle technical execution), solving problems that block customer success but arent 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 wed learn something new.

Strong solutions frequently get upstreamed into:

- Core platform capabilities

- Reusable internal tooling

- Product roadmap items owned by core engineering

Core Engineering :

- Product Roadmap

- Roadmap and sprint-driven

- Deep, well-scoped, long-lived

- Platform & system optimisation

- Slower, abstracted

- Platform Abstractions & scalability

Customer Impact (This Role) :

- Day-to-day customer usage & success signals

- Signal-driven; flexible planning

- Broad, evolving, discovery-heavy

- Unblocking + generalising into scale

- Immediate, real-world

- Reduction of future friction & repeat issues

Why ambitious engineers choose this role :

Unmatched learning velocity :

- Build with cutting-edge AI (LLMs, prompt engineering, RAG) in production

- Learn what actually makes products succeed in real markets

- Develop full-stack skills alongside business judgment

Real Autonomy:

- Choose your own tools and approaches

- Ship when ready - not when a sprint ends

- Direct influence on the product roadmap & org metrics via real usage

Career acceleration:

- Engineers from this track commonly become:

- Product Engineers who can both build and prioritise

- Technical Leads who deeply understand customer reality

- Founding Engineers or CTOs at startups with a complete skill stack

Immediate impact:

- Direct feedback from real usage

- Clear line from your work to business outcomes

What were looking for:

Technical baseline:

- Strong programming fundamentals (Python / JavaScript preferred, but talent > language)

- Comfort with ambiguity and incomplete requirements

- Curiosity about AI / ML in production (expertise is a bonus, not a requirement)

Mindset fit:

- Ownership: Takes end-to-end responsibility for outcomes, not just tasks; drives problems to resolution even across unclear boundaries.

- Pragmatic: Focused on solutions that work now

- Curious: Wants to understand the why behind problems

- Entrepreneurial: Treats technical problems as business problems

- Communicative: Can translate technical decisions into a business context

- AI Native: Exposure to AI systems or prompt & context engineering.