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Position Overview

We are seeking a rare hybrid profile: a senior, product-minded, AI-native engineer who can own a product area end-to-end, from a vague business objective through thoughtful product definition, technical design, implementation, testing, and iteration. This person should operate less like a ticket-based contractor and more like a founding engineer assigned to a product area. The ideal candidate can understand user needs, define effective workflows, make sound SaaS architecture decisions, use AI tools to accelerate high-quality development, validate and test generated code, communicate clearly, and move work forward independently in ambiguous environments.

Responsibilities

- Own product areas from broad business goals through discovery, design, implementation, testing, and iteration.
- Translate ambiguous objectives into explicit user workflows, product requirements, technical plans, and implementation steps.
- Question unclear requirements, identify missing pieces, surface risks and edge cases, and propose better product or technical approaches when appropriate.
- Design and build polished, intuitive SaaS workflows, including dashboards, tables, filters, forms, detail views, configuration experiences, empty states, loading states, and error states.
- Make thoughtful architecture decisions across data models, APIs, frontend architecture, backend architecture, permissions, integrations, state management, scalability, security, and maintainability.
- Use AI-assisted development tools to accelerate coding, refactoring, debugging, test creation, documentation, and architectural exploration.
- Critically review, validate, debug,



and test AI-generated code to ensure production-quality implementation.
- Communicate clearly and proactively about requirements, architecture, tradeoffs, implementation status, open questions, risks, and blockers.
- Drive work forward independently while collaborating effectively with product, engineering, design, and business stakeholders.

Duties:

Qualifications

- Senior-level engineering experience with the ability to design, build, validate, and improve production software.
- Solid product judgment, including the ability to understand users, workflows, pain points, business outcomes, and success criteria.
- Demonstrated ability to operate in ambiguity, ask clarifying questions, challenge assumptions respectfully, and make progress without highly detailed tickets.
- Advanced fluency with AI-assisted development tools such as Cursor, Claude, ChatGPT, GitHub Copilot, or similar environments.
- Ability to orchestrate AI tools effectively rather than treating them as simple autocomplete or boilerplate generators.
- Strong technical judgment across SaaS architecture, including data modeling, API design, frontend and backend design, permissions, integrations, scalability, error handling, maintainability, and security considerations.




- Solid understanding of modern SaaS UX patterns and the ability to build interfaces that feel intuitive, polished, and product-quality.
- Excellent written communication skills, with the ability to explain reasoning, tradeoffs, risks, blockers, and decisions clearly and concisely.
- Ownership mindset with the ability to act as a force multiplier for the product and engineering organization.

Additional Knowledge & Skills

- Ability to define product workflows, key screens, data models, permissions models, user actions, edge cases, and implementation approaches from vague product goals.
- Experience using AI for code generation, architecture comparison, debugging, refactoring, test creation, documentation, and rapid iteration.
- Ability to identify hallucinations, flawed logic, architectural gaps, and quality issues in AI-generated output.
- Strong instincts around SaaS admin workflows, multi-tenant considerations, auditability, notifications, readiness tracking, executive summaries, and operational dashboards.
- Comfort evaluating tradeoffs between MVP scope, future scale, usability, performance, security, and maintainability.
- Ability to communicate asynchronously with clarity, structure, and appropriate context for technical and non-technical stakeholders.
- Strong candidates will show evidence of owning product, UX, architecture, and implementation together, rather than operating only as a backend, frontend, or ticket-based individual contributor.
- This role is not a fit for someone who requires fully defined requirements, avoids ambiguity, cannot explain architectural decisions, has weak written communication, or cannot use AI tools at an advanced level.

📌 AI Native Product Engineer (India)
🏢 Cyclotron
📍 India