Principal Engineer - Applied AI

Responsibilities:

• Set and evolve the reference architecture across the Lighthouse control plane, Context Layer, data and execution adapters, workflow and agent runtime, evaluation, and production operations.
• Define the contracts that keep the platform portable: canonical business concepts and tenant mappings; reusable.
• Context Modules and task Recipes; context requests, plans, packages, and traces; and the runtime boundary between context compilation and data execution.
• Own security and deployment architecture for shared, dedicated, and customer-hosted environments, including identity, authorisation, tenant isolation, secrets, network boundaries, data residency, audit, and policy enforcement before retrieval and action.
• Design reliable execution for deterministic and model-driven workflows, including long-running state, approvals, retries, idempotency, timeouts, partial failure, compensation, safe replay, and rollback.
• Establish engineering standards for AI quality and system operations: evaluation datasets, regression gates, tracing, evidence and grounding, service-level objectives, incident learning, latency and cost budgets, and model or tool fallback.
• Make build-versus-buy and open-source extension decisions with clear upgrade paths; turn proven patterns into versioned services, APIs, connectors, SDKs, and solution templates rather than premature abstractions or customer-specific forks.
• Personally design, implement, and review the components with the greatest architectural, security, performance, or operational risk; mentor engineers and communicate decisions clearly to product, business, and customer stakeholders.

Requirements:

• 8+ years building production software, including principal-level technical leadership across distributed, data-intensive, platform, or enterprise systems.
• Deep hands-on strength in Python, API and service design, relational data modelling, asynchronous and event-driven systems, and cloud-native operations.
• Experience defining architecture across multiple services and deployment modes, including identity, authorisation, multi-tenancy or customer isolation, observability, and failure recovery.
• Experience taking AI or ML systems beyond prototypes, including structured model outputs, tool use, retrieval or context, evaluation, guardrails, and production debugging.
• Strong judgment about system boundaries, data ownership, consistency, scalability, security, build-versus-buy choices, and how much abstraction is justified at each stage.
• A track record of leading through influence while staying close to implementation, code quality, production behavior, delivery sequencing, and customer outcomes.