Principal Engineer II

As a Principal Engineer II, you will set the technical direction for the Neo suite and mentor senior engineers across teams. We are proponents of leveraging best-in-breed AI tools at every step of the engineering lifecycle — design, development, review, and testing. This requires a different engineering mindset: a lean team of senior, hands-on, AI-adept engineers, resulting in ultra-rapid iteration cycles and fast output. You will own, oversee, and set the bar for this approach across the suite, from design through to production. This is a role for an independent decision-maker who makes architectural calls that span products and are hard to reverse, and who raises the bar for what both people and agents ship.

The candidate will have responsibilities across the following functions:

Architecture and technical direction:

• Own the architecture of the Neo suite across backend and frontend — service boundaries, data models, and the shared platforms every product builds on.
• Set multi-year technical direction for the suite, and make the build-vs-buy, vendor, and model decisions behind it.
• Own how agents act inside the product — tool and MCP integrations, context and permissions, and the cost and latency of model calls at scale.
• Resolve the cross-cutting reliability, performance, and security concerns that no single team owns.

Execution and delivery:

• Drive delivery end to end: break a spec down, run agents in parallel to implement, and steer them to a working result.
• Own verification — the real bottleneck in an agentic workflow. Decide what "correct" means for a system and make sure what ships meets it.
• Lead the highest-risk work across teams — migrations, re-architecture, major incidents — through to production.

AI-first SDLC harness:

• Build and improve the harness engineering uses — specs, reusable skills, subagents, evals, and guardrails that let many agents work in parallel with consistent results.
• Set the org-wide standard for agent use: what agents may change, and the quality gates their changes pass before a human reviews them.

Mentoring and raising the bar:

• Mentor senior engineers and run design reviews; turn what works into standards and paved roads.
• Spread good agent practice across teams so its leverage compounds, not just your own.

Product thinking:

• Work with product to pin down the right problem before building — scope feasibility, surface trade-offs, and push back when the cost isn't worth the outcome.
• Own engineering outcomes across the suite: reliability, cost to serve, and delivery speed.

Requirements:

• AI-native engineering practice: Fluent in an agentic workflow: spec-driven delivery, steering coding agents, and building the harness that makes their output reliable. Experienced in defining how an engineering org uses agents.
• AI system architecture: Agent orchestration, tool and MCP integration, context and memory design, and evals in production; managing LLM cost, latency, and failure modes.
• Language and runtime fundamentals: Deep proficiency in one or more of Java, Python, TypeScript/JavaScript, or Go, including concurrency, memory management, and runtime behaviour under load. Picks the right language for the job.
• System internals: Working knowledge of processor, memory, network, and storage internals, and how to write code that exploits hardware well and avoids bottlenecks.
• Distributed systems: Fault modelling, concurrency, isolation, and consensus; time, clocks, and ordering of events; rate control and load distribution. Strong design and problem-solving instincts for performance, scalability, security, and reliability.
• Scale and multi-tenancy: Multi-region, multi-tenant SaaS, designed for tight p99 latency and low cost to serve.
• System internals and performance: Processor, memory, network, and storage internals; non-blocking I/O; diagnosing memory issues, GC tuning, and resource leaks.
• Troubleshooting and performance: Diagnosing memory issues, GC tuning, and resource leaks to keep systems stable and efficient under load.
• Core infrastructure: Hands-on with the internals of systems such as Kafka, Cassandra/Scylla, and Redis — enough to troubleshoot, tune, and make sound usage decisions.
• Orchestration and protocols: Kubernetes and containerised deployment on AWS, Google Cloud, or Azure; gRPC, HTTP/2, and QUIC.

Experience and Qualifications:

• 15+ years in software development and delivery, with a track record of setting technical direction across multiple teams or products.
• Led at least one large architecture change end to end — a migration, re-platform, or new core system.
• Production experience across a modern stack — e. g., Java, Python, or Node/TypeScript — with REST, SQL (PostgreSQL or MySQL), messaging systems, and microservices.
• Engineering degree in computer science or equivalent experience.