About The Role

ABOUT THE ROLE

This is a full stack engineering role at an early-stage AI/consumer intelligence SaaS startup, where you will own features end-to-end across backend services and frontend interfaces. The team is small, so full stack genuinely means full ownership: you will take features from idea to production and iterate directly with enterprise clients.

WHAT YOU'LL DO

Build and ship features across the full stack, using Go or Python on the backend and React/TypeScript on the frontend.
Design APIs, data models, and service architectures that support agentic AI capabilities.
Create intuitive interfaces that translate complex enterprise data into clear, actionable workflows.
Collaborate with ML engineers to bring AI-driven features to production.
Own features through their full lifecycle: scoping, architecture, implementation, testing, deployment, and iteration.
Work directly with enterprise customers and stakeholders to understand real-world needs and refine the product.
Contribute to infrastructure, tooling, and developer experience as the engineering team scales.

WHAT WE'RE LOOKING FOR

3 or more years of professional engineering experience with meaningful work across both frontend and backend.
Proficiency in TypeScript/React and at least one of Go or Python, with demonstrated shipping experience in both layers.
Experience with AWS or equivalent cloud infrastructure and modern deployment practices.
Experience building and deploying data pipelines or data engineering work.
Comfortable owning loosely scoped problems and managing ambiguity without extensive guidance.
Background in early-stage startup environments where you built and shipped products end-to-end.
Strong product instincts: you think about the user, not just the code.
A bachelor's degree in Computer Science or a related field.
Nice to have: experience with LLM integrations, agentic AI systems, or RAG architectures; background in enterprise SaaS, retail technology, or data-intensive products; familiarity with data visualization, real-time systems, or streaming architectures.