Role: Product Engineering Tech Lead
Experience: 3 - 5 years
Location: Bengaluru
Role Summary
We are building an AI-native B2B intelligence and procurement platform. You will lead the Product Engineering pod, acting as the bridge between our backend AI/Agent engines and the end-user. You will own the architecture and delivery of the full product—translating complex agent workflows, search results, and procurement lifecycles into a stable, secure, and seamless application
This is a hands-on technical leadership role (70% IC). You will actively code and build core product features while setting the engineering standards for the team.
Roles & Responsibilities
- Lead the architectural design and development of the full-stack product (React/Vite frontend, FastAPI backend
- Own the end-to-end procurement workflow lifecycle natively in the app (from RFQ to QuotetoPO).
- Integrate backend agent outputs and ML attributes smoothly into user-facing applications with real-time feedback loops.
- Design robust business rules, role-based access control (RBAC), permissions, and audit trails.
- Oversee cybersecurity, application security, and secure deployment practices.
- Optimize application performance to handle complex comparisons, rich filtering, and heavy data loads.
- Mentor full-stack engineers and ensure high standards of code quality, testing, and system reliability.
Skills Required
Must Have:
- 6+ years in full-stack software engineering or product engineering roles.
- Deep hands-on experience with modern frontend frameworks (React) and backend Python frameworks (FastAPI, Django, or similar).
- Experience designing complex transactional systems, stateful workflows, or SaaS platforms fromscratch.
- Solid understanding of PostgreSQL, API design, and systems architecture.
- Experience implementing enterprise-grade security, permissions, and auditlogging.
- Strong product sense—you care deeply about how backend architecture impacts the user experience.
Good To Have
- Experience building B2B marketplaces, procurement tools, or enterprise SaaS.
- Familiarity with integrating LLMs, streaming responses, or AI agents into user interfaces.
- Experience with cloud deployments, Docker, and CI/CD
pipelines.
