Product Engineer - I (AI Agents)

As a Product Engineer - I, 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 our 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.

Apply even if youre only 70% sure. The conversation itself is valuable.

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.

Run customer calls

Own accounts

Chase stakeholders

Do repetitive support work

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

Your success is measured by reduced future friction - not by how many customers you personally unblock.

Code quality: Same high bar Technical difficulty: Often harder - real-world problems are messier and more near-term in nature than planned roadmaps

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

Both are real engineering. Both lead to staff or lead roles. Both are equally respected.

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

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.

YES, if you:

Want to work on AI-native products at the frontier

Enjoy variety - no two weeks look the same

Care about understanding the why behind what you build

Prefer building solutions over strictly following specs

Are curious about how engineering decisions affect business outcomes

MAYBE NOT, if you:

Prefer predictable, tightly defined work

Dislike exposure to business or customer context

Need detailed specs to be productive

Prefer large teams over small, autonomous pods

Want to avoid ambiguity or rapid context-switching

No two weeks look the same, but a typical flow might look like this:

Monday: Pod sync to review customer progress and technical blockers. One issue stands out - a workflow the product doesnt fully support yet.

Tuesday: Prototype a solution (integration, tool, or AI workflow). Loop in core engineering early if it looks reusable.

Wednesday: Refine the solution - edge cases, AI prompt behaviour, reliability. Decide whether this should remain custom or be generalised.

Thursday: Ship to production. Monitor real usage. Iterate quickly if needed.

Friday: Share learnings, move on to the next.

Freedom level: High

Bureaucracy: Low

Learning curve: Steep

Impact visibility: Immediate and measurable

Compensation: Competitive!

Joining: ASAP!

We believe that long-term, people over product and profits, prioritize culture over everything else.

Freedom and Responsibility

Entrepreneurial Team

Exponential Growth

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