Staff AI Engineer
4 days ago
As a Staff AI Engineer, you will be a technical leader responsible for designing and implementing the core architectural components of the *AI Workbench platform* and ensuring all delivered applications achieve sustained production quality and scale. You will work within *"small, skilled AI-native teams"* using a *"Human + AI Co-creation"* approach to deliver products and AI agents rapidly, in weeks, not months.Your focus will be on the critical journey from a rapid MVP to sustained production.### Key Responsibilities1. *Architectural Leadership for Scale and Reliability:* * Lead the design and implementation of the capabilities necessary to ensure applications become *robust, scalable, and continuously managed in production*. * Ensure the platform is designed for *seamless growth* on demand. * Drive the architectural structure of the AI Workbench, ensuring it is built using *Model Context Protocol (MCPs)* to maximise *composability* and modularity.2. *End-to-End Application Lifecycle Ownership:* * Take responsibility for the *entire application lifecycle management*. * Design and implement core features that handle the complex capabilities required for continuous operations, including *deployment, operations, monitoring, and support*.3. *Proprietary Generative AI Economics:* * Design, implement, and maintain the innovative *"thought caching" mechanism* within the *Agentic Core* of the AI Workbench. * Solve the significant barrier of the *high and sometimes prohibitive costs of Large Language Model (LLM) access for large-scale use cases. This mechanism is a **key differentiator* that provides a *significant cost advantage to customers*.4. *Addressing Talent Scarcity:* * Contribute to overcoming the global scarcity of *"AI talent"* by mentoring team members and establishing best practices within the AI-native teams.### Required Qualifications* Extensive experience successfully moving sophisticated software (ideally AI-based solutions) from initial development (MVP) to a *robust, scalable, and continuously managed application in production*.* Proven ability to design and implement complex architectural components, such as agents and proprietary core mechanisms (e.g., thought caching), that provide a competitive advantage and serve as a *key differentiator*.* Deep technical understanding of the complexities associated with LLM access, performance, and the economic barriers to scaling AI applications.* Experience in architectural principles that support sustained growth, including modularity and maximising *composability*.* A strong desire to operate in an entrepreneurial environment, tackling fundamental challenges related to production complexity and application economics (Conversation History).
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