• Stay close to the tasks, acceptance criteria, quality risks, traffic patterns, customer feedback, and incidents of the products that use the platform • Own the request path: API contracts, authentication (OAuth2), role-based access control, tenant isolation, policy, routing execution, fallback, streaming, and metering • Design and build secure REST APIs and streaming endpoints, and backend services that stay correct under concurrency, partial failure, and traffic spikes • Build the web control surfaces that make a complex system legible, so an operator can see state and act on it without reading source code • Integrate and operate external model providers and self-hosted inference endpoints, and maintain a versioned catalogue of approved models, drivers, capabilities, limits, and prices • Measure serving performance under realistic prompt lengths, output lengths, concurrency, and traffic mixes, then plan capacity and optimise configuration and cost • Build small task-specific models, prompts, retrieval flows, and agent drivers for well-defined product or customer tasks, then expose them through the platform • Define task-level quality and unacceptable behaviour with product teams and domain experts, and assemble representative evaluation and holdout datasets • Compare candidate models and drivers against the current baseline on task success, safety, latency, and cost before shifting traffic • Implement policy-aware routing, confidence or uncertainty escalation, safe fallbacks, and customer-specific controls where the evidence justifies them • Use shadow traffic, staged releases, regression evaluations, monitoring, and rollback to introduce new serving or intelligence behaviour safely • Track production failures and feedback, then improve the whole system: data, prompts, models, tools, policies, capacity, and operational controls • Model data carefully in PostgreSQL, and measure usage and cost accurately enough to report and bill from • Protect tenant data throughout the request path, respecting privacy, residency, and model-use boundaries • Maintain dashboards, alerts, runbooks, incident response, and clear ownership for the platform and the capabilities it serves • Work with product teams, AI engineers, domain experts, infrastructure, and security to make capabilities reusable across products • Keep team knowledge written down: technical requirements, API contracts, design notes explaining model choice and routing policy, deployment notes, and post-mortems • Mentor other engineers and raise the bar on design and code quality through review •
