Staff AI Engineer

Forbes Advisor is an initiative for consumers under the Forbes Marketplace umbrella that provides journalist- and expert-written insights, news and reviews on all things personal finance, health, business, and everyday life decisions. We do this by providing consumers with the knowledge and research they need to make informed decisions they can feel confident in, so they can get back to doing the things they care about most. If you're looking for challenges and opportunities similar to those of a startup, with the benefits of a seasoned and successful company, then read on:

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 behaviour.

Requirements:

• 10 - 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 who 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.