fonoa - Senior/Staff Software Engineer, AI
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Requirements
• 5+ years building customer-facing products at a high-quality software company • Strong track record of delivering complex, end-to-end product features with visible impact • Ability to shape problems and drive solutions independently with high ownership • Deep understanding of LLMs, prompt engineering, agent frameworks, and AI safety/security practices • Experience with RAG architectures and model evaluation • Strong product and engineering instincts, with the ability to balance speed, quality, and pragmatism • Comfortable operating in fast-moving, ambiguous environments • Proven ability to experiment and deliver in an evolving technology landscape • API DOCUMENTATION • For engineers, API design and structure are an important part of evaluating a role. You can explore our API and how we build products here: https://docs.fonoa.com/ • We use these technologies today, but we don’t hire for specific tools. We care about engineers who can understand problems, make good technical decisions, and choose the right tools for the job. • Backend: Node.js, Go • Frontend: React, TypeScript • Infrastructure: GCP, k8s, Terraform, Postgres • If you don’t think you have the full experience we’re looking for, but you could be a fit and are willing to learn, do apply anyway! We are building a diverse and inclusive team. • As part of the recruitment process at Fonoa, we process your personal data in accordance with our Privacy Notice for Job Applicants https://www.fonoa.com/legal/privacy-notice-job-applicants. This notice explains how and why your data is collected and used, and how you can contact us if you have any concerns.
Responsibilities
• Build AI-powered product features that feel native, fast, and intuitive to use • Partner closely with product and design to prototype and iterate on intelligent workflows and user interactions • Design backend services powering natural language interfaces, smart suggestions, and agentic systems • Optimize prompts, shape model behaviour, and improve evaluation pipelines • Build and evolve internal and external agent platforms • Enable both technical and non-technical teams to leverage AI more effectively • Continuously experiment, iterate, and improve AI systems in production
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