Principal AI Architect

SecurityScorecard•New York, NY

8h•$270,000 - $330,000

About The Position

SecurityScorecard is hiring a Principal Software Architect to lead the system design of our agentic AI capabilities and serve as a technical anchor for engineering teams building the next generation of our platform. This is an individual contributor role reporting to the Chief Architect, alongside a Front-end Architect and a Data-focused Architect, and partnering closely with engineering leadership, Product, and Data Science. We're looking for someone who can go deep technically and also move a room: an architect whose designs get adopted because they're well-reasoned and well-communicated, not just handed down. You will lead the system design for how agentic features are built, integrated, and operated reliably at production scale, working in close partnership with our Data Science team, who bring the model and ML depth. Your focus is the system: orchestration, state and memory, tool boundaries, failure semantics, and how all of it composes with the wider platform. Like the rest of our architecture function, you'll prototype to prove out decisions rather than implement full solutions, and you'll set direction through Technical Design Reviews (TDRs), and standards. Deep data-engineering specialization isn't required, but you should be comfortable reasoning about data flows, storage decisions, and how agentic systems consume and act on data.

Requirements

10+ years of software engineering experience, including significant time as a senior, staff, or principal-level architect or technical lead designing production systems
Deep expertise in distributed systems: service boundaries, consistency models, event-driven architecture, API design (REST; gRPC familiarity a plus), and operating systems reliably at scale
Hands-on experience building, prototyping, and architecting agentic or LLM-powered systems in production, not just demos: agent loop design, context engineering, MCP and tool/function-calling integration, RAG architectures, memory and state management, and reliability and guardrail patterns for non-deterministic systems
Working knowledge of data engineering fundamentals, including data modeling, pipeline design, and streaming vs. batch, sufficient to make informed architectural calls without deep specialization in the domain
A track record of influence without authority: presenting a technical direction to skeptical engineers and earning genuine buy-in, and giving rigorous design review feedback on systems you didn't build yourself
Strong technical writing and mentorship: TDRs, design docs, and decision records that teams can act on without hand-holding, plus a history of raising the technical bar around you
Ability to communicate complex architectural decisions clearly to executives and customers
Comfort operating as a senior individual contributor, driving outcomes through prototyping and technical credibility

Nice To Haves

Familiarity with multi-agent orchestration patterns (supervisor and sub-agent architectures, agent handoffs), which our use cases are likely to grow toward
Experience with our stack or comparable technologies: Node.js/TypeScript, PostgreSQL, ClickHouse, Kafka, AWS, Kubernetes
Experience leading AI-assisted or agentic development adoption across an engineering organization; given the scope of this work at SecurityScorecard, this carries significant weight
Experience partnering closely with data science or ML teams on productionizing their work
Cybersecurity industry background

Responsibilities

Own the system design for our agentic capabilities: orchestration, context and state, tool boundaries, and reliability patterns for non-deterministic systems
Write the TDRs, design docs, and standards that set architectural direction for agentic work across teams
Prototype to validate architectural decisions before teams commit to them at scale
Partner with Data Science so that agent and model work composes cleanly with existing platform services, from APIs and event streaming through to storage
Review TDRs from across engineering, giving teams substantive feedback on architecture and risk, not only on AI work
Build consensus across engineering teams and get them genuinely aligned, not merely compliant
Help drive the shift to AI-assisted and agentic development across engineering, shaping how engineers design, review, and iterate in an AI-native workflow
Mentor senior and staff engineers on agentic system design and measurably elevate the technical bar
Communicate architectural decisions and their trade-offs clearly to executives and customers