Product Engineer - I

SquadStack.ai; Dadri; On-site; INR 1,200,000 - 1,800,000; Full time

Benefits offered by this job

Competitive compensation

Freedom and Responsibility

Healthcare benefits

Job summary

An innovative technology company in India is seeking a Product Engineer - I to develop production-grade Voice AI systems. This role involves collaborating closely with customer success teams to understand real deployment challenges and drive engineering solutions to improve customer outcomes. Candidates should be comfortable with ambiguity, possess a curiosity about AI, and take ownership of their projects. The organization values engineers who focus on building meaningful solutions rather than merely following specs.

Qualifications

• Comfort with ambiguity and incomplete requirements.
• Curiosity about AI/ML in production, expertise a bonus.
• Ownership of outcomes across unclear boundaries.

Responsibilities

• Building tools and AI workflows that unblock customer needs.
• Designing AI prompts and LLM-based solutions.
• Collaborating with core engineering to reduce friction.

Skills

Comfort with ambiguity

Curiosity about AI/ML

Ownership of outcomes

Pragmatic problem-solving

Curiosity

Entrepreneurial mindset

Communicative skills

Exposure to AI systems

Job description

As a Product Engineer - I at SquadStack.ai, 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 SquadStack 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.

• Building custom integrations, tools, and AI workflows that unblock customer needs
• Designing and refining AI prompts, RAG systems, and LLM-based solutions
• 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:

• Own accounts
• Chase stakeholders

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 upstreamed into:

• 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.

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

Two engineering tracks - both valuable:

Core Engineering:

• Product Roadmap
• Roadmap and sprint-driven
• Deep, well-scoped, long-lived
• Platform Abstractions & scalability

Customer Impact (This Role):

• Day-to-day customer usage & success signals
• Broad, evolving, discovery-heavy
• Immediate, real-world
• Reduction of future friction & repeat issues

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

Why ambitious engineers choose this role

• 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
• 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

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
• Direct feedback from real usage
• Clear line from your work to business outcomes

What we’re looking for

Technical baseline:

• Comfort with ambiguity and incomplete requirements
• Curiosity about AI / ML in production (expertise is a bonus, not a requirement)
• 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.

Should I apply?

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
• I am 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
• Want to avoid ambiguity or rapid context-switching

Day-to-day reality

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 doesn’t 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.

Quick Check?

• Freedom level: High
• Bureaucracy: Low
• Learning curve: Steep
• Impact visibility: Immediate and measurable

Logistics

• Location: Noida
• Compensation: Competitive!
• Joining: ASAP!

Why should you consider us seriously?

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

• We are a well-balanced team of experienced entrepreneurs and are backed by top investors across India and Silicon Valley (Chiratae Ventures, Blume Ventures, Abstract Ventures, Emergent Ventures; Senior execs at Google, Square, Genpact & Flipkart; Co-founders of Infosys, Snapdeal, Slideshare, Zomato, etc.)
• Freedom and Responsibility 🦅
• Healthcare (Physical & Mental Wellness) 😌

Please Note:

SquadStack is committed to a diverse and inclusive workplace. SquadStack is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.