Responsibilities

Own end-to-end delivery of AI-powered products — from problem framing and architecture through implementation, deployment, and production operations
Design, build, and ship full-stack systems: frontend, backend APIs, data layers, and AI/ML services — not just models or prompts
Take a product from idea to production independently: design APIs, build UIs, wire data flows, integrate models, and operate the system
Build and productionize LLM / GenAI features (RAG, agents, tool-calling, evaluation, guardrails) and integrate them into real user-facing applications
Design scalable application and model-serving architectures that are reliable, observable, and cost-aware
Own data pipelines, retrieval, embeddings, and evaluation loops so AI features stay accurate and measurable in production
Work across the stack as needed — React/Next (or equivalent), Node/Python/Go services, databases, queues, cloud, and CI/CD — without waiting on a specialist for every layer
Partner with product, design, and engineering to turn ambiguous problems into shipped features
Review code, raise the bar on system design, and mentor engineers on both AI and full-stack practices
Establish engineering standards for AI quality, evaluation, security, and release confidence
Debug production issues across the full path: UI, API, infra, data, and model behavior

Requirements

15+ years of software engineering, with recent hands-on ownership of production systems
Proven full-stack depth: can design and implement frontend, backend, APIs, data stores, and cloud infrastructure — not limited to AI-only work
Hands-on with AI-assisted development tools (Cursor, GitHub Copilot, and similar) as part of day-to-day engineering
Hands-on experience shipping LLM / GenAI or ML systems in production (RAG, agents, fine-tuning, evaluation, or equivalent)
Comfortable choosing and using the right stack for the problem (e.g. TypeScript/Python, React or similar, REST/GraphQL, SQL/NoSQL, queues, object storage)
Experience with CI/CD, distributed systems, and production debugging
Strong ownership: can take an ambiguous brief and deliver a working product end to end
Clear communication and the ability to work with product, design, and other engineers without hand-holding

Qualifications

Degree in Computer Science / Engineering or equivalent practical experience
Hands-on engineer with a production-level coding mindset — writes, reviews, and ships code
Track record of building scalable, reliable systems, not just prototypes or notebooks
Good documentation and collaboration skills
Bias toward owning the whole problem: product, architecture, implementation, and operations.

Perks:

Staff AI Engineer

About the Role