About the role
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 doYou’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.
- Run customer calls
- Own accounts
- Chase stakeholders
- Do repetitive support work
- Can this be reused?
- Can this become a product primitive?
- Can this eliminate future manual work?
- Core platform capabilities
- Reusable internal tooling
- Product roadmap items owned by core engineering
Code quality: Same high barTechnical difficulty: Often harder - real-world problems are messier and more near-term in nature than planned roadmapsTwo engineering tracks - both valuable:Core Engineering:
- Product Roadmap
- Roadmap and sprint-driven
- Deep, well-scoped, long-lived
- Platform & system optimisation
- Slower, abstracted
- Platform Abstractions & scalability
- 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
- 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
- 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
- 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)
- 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.
- 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
- 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
- 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.
- Freedom level: High
- Bureaucracy: Low
- Learning curve:Steep
- Impact visibility: Immediate and measurable
- Location: Noida
- Compensation: Competitive!
- Joining:ASAP!
- 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 🦅
- Entrepreneurial Team 💪
- Exponential Growth 📈
- Healthcare (Physical & Mental Wellness) 😌