Full Stack AI Engineer

We're looking for a Full Stack AI Engineer to own complete AI-native systems from concept through production at a fast-moving enterprise AI platform. This means product thinking, UX, frontend, backend, AI workflows, infrastructure, deployment, and iteration — all of it, not a slice of it.

This is not a narrow frontend, backend, or machine learning role. You will take loosely defined business problems, design the user experience, decide how the system should work, build every layer, deploy it securely, measure whether it works, and improve it based on real usage. The hardest thing to find for this role is not technical skill — it is systems thinking. If you think beyond individual tickets, consider second-order effects before making technical decisions, and prefer ownership and ambiguity over narrowly scoped work, this role is a strong fit.

Why You'll Want to Join

You will be paid in USD (bi-monthly: every 15th and 30th) Paid Time Off in accordance with company policy Observance of Holidays per company guidelines 100% remote setup so you can work wherever you're most productive This role requires availability during US business hours Own large parts of the product and architecture at an early, high-leverage stage Work directly with enterprise customers and see the real impact of what you build Help establish the engineering foundation of a company building AI infrastructure that influences real decisions

What You'll Work On

End-to-End Product and System Ownership

Build complete AI-native products from concept through production Turn loosely defined business problems into clear product and technical systems Design polished, simple interfaces for complex enterprise workflows Zoom in to debug a broken API call and zoom out to question whether the entire workflow is designed correctly Make architecture decisions based on scale, reliability, security, speed, and long-term maintainability

Frontend and Backend Development

Build responsive frontend applications and reusable component systems using React and Next.js Build secure APIs, backend services, data pipelines, databases, and asynchronous workflows Design systems for multi-tenancy, role-based access, auditability, data isolation, and enterprise security Connect the platform to enterprise systems including HRIS, ATS, CRM, email, calendars, and internal databases

AI and LLM Engineering

Develop LLM-powered features, agents, retrieval systems, evaluation pipelines, and decision workflows Build production AI systems with strong understanding of RAG, embeddings, tool calling, structured outputs, agent workflows, prompt design, model evaluation, and failure handling Design and evaluate AI workflows across multiple models and providers, using techniques such as adversarial review, model-to-model critique, and systematic evaluation to identify failure modes and improve reliability Design systems that operate inside customer VPCs, private clouds, and controlled enterprise environments Use AI coding tools aggressively to move faster without allowing generated code to reduce quality or understanding

Infrastructure and Observability

Deploy, monitor, and maintain cloud-based applications across GCP, AWS, Azure, and customer VPC environments Build observability, testing, logging, alerting, and evaluation systems so issues are caught before customers notice Design for CI/CD, infrastructure as code, and secure deployment pipelines Own authentication, permissions, encryption, secrets management, and data privacy across the stack

What You Bring

5 or more years of professional software engineering experience with a track record of building and shipping real production systems, ideally from zero to meaningful scale Strong frontend experience with React, Next.js, and TypeScript Strong backend experience with Python, TypeScript, Node.js, or similar technologies Hands-on experience building production AI or LLM features, not just prototypes or API wrappers Deep understanding of RAG, agents, tool use, memory systems, context management, and evaluation frameworks Experience working across multiple LLMs and model providers, with a strong understanding of model selection, evaluation, adversarial testing, and the tradeoffs between quality, reliability, latency, and cost Strong AI-native development practices, using tools such as Claude Code, Cursor, Codex, or similar, with a clear approach to continuously testing and adopting new AI models, tools, and workflows Experience designing APIs, databases, data models, queues, event-driven systems, and distributed workflows Strong product judgment and enough design taste to turn complicated workflows into interfaces that feel obvious Strong understanding of authentication, permissions, encryption, secrets, data privacy, and secure software design High ownership — you do not wait for someone else to define every requirement, catch every edge case, or clean up after launch Clear written and verbal communication in an async, distributed environment

Nice to Have

Experience with PostgreSQL, Redis, vector databases, and search systems Familiarity with OAuth, SSO, SAML, SCIM, RBAC, audit logs, and enterprise integrations Experience with Docker, Kubernetes, and infrastructure as code Background deploying inside customer VPCs or private networking environments Experience with Figma or the ability to independently create strong product flows and interfaces Prior experience as a founding engineer or primary engineer at an early-stage company

How to Apply

Please include:

Your updated resume A portfolio link or GitHub showing AI systems or full stack projects you have built and shipped in production A short Loom video (1 to 2 minutes) introducing yourself, walking through a system you are most proud of, and explaining the architectural decisions you made — with a focus on how you thought about the system as a whole, not just individual components

Only candidates who submit both a portfolio and Loom video will be moved to the next step of the hiring process.

If you are a full stack AI engineer who thinks in systems, can own the entire product lifecycle independently, and wants to build AI infrastructure that influences real decisions at enterprise scale, this role gives you the ownership and the direct impact to do your best work.