Full Stack Engineer (AI/Enterprise SaaS)

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.