Principal AI Architect

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real content. We need to parse the posting. We have: - About SecurityScorecard: description of company. Should we include as "About the role"? Actually "About the role" is a heading we can use for description of role. The "About SecurityScorecard" is company overview; maybe we can put under "About the role"? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have "About the role" heading and include description of role and maybe company overview? Usually "About the role" includes description of role and maybe company. We'll include the role description text after "About the Role:" heading. We have "About the Role:" paragraph describing role. We'll put that under

About the role

with

. Then responsibilities: bullet list under "What You'll Do". That's a list of duties. We'll put under

Responsibilities

with

• each bullet. Then qualifications: "Required Qualifications:" list. We'll put under

  Required Qualifications

  maybe heading "Requirements"? The instruction list includes Requirements as a possible heading. We can use "Requirements". We'll put required qualifications there. Then "Preferred Qualifications:" list. Could be under "Qualifications" or "Preferred Qualifications". We have both Required and Preferred. Could combine under "Qualifications" with subheadings? But instruction says use headings from list; we could have "Qualifications" and include both required and preferred as paragraphs/lists. But better to separate? The instruction says include a section ONLY when posting has real, specific content for it. It doesn't forbid multiple similar sections; we could have "Required Qualifications" and "Preferred Qualifications" but those aren't in the allowed list. Allowed list includes Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we should map required qualifications to "Requirements" heading, and preferred qualifications to "Qualifications" heading (or maybe Skills). But "Qualifications" heading is allowed. We'll put preferred qualifications under

  Qualifications

  . Skills maybe not explicitly listed; we could omit Skills if no specific skills list beyond what's in qualifications. There's a "Our Tech Stack" section describing technologies; could be considered Skills. We could put that under Skills heading. But we need to decide. We have "Our Tech Stack" describing Node.js/TypeScript, React Microfrontend, PostgreSQL, ClickHouse, Kafka, AWS, Kubernetes, etc. That's a list of technologies; could be considered Skills. We'll put under

  Skills

  with
  • . Benefits: there is a Benefits paragraph: "Specific to each country, we offer a competitive salary, stock options, Health benefits, and unlimited PTO, parental leave, tuition reimbursements, and much more!" plus compensation range. So we have Benefits and Pay. We'll put Benefits heading with that paragraph. Pay heading with compensation range: "The estimated total compensation range for this position is $270,000 - $330,000 (base plus bonus)." Also mention base plus bonus etc. We'll include that. Schedule: not mentioned; we should not include Schedule heading. We must not include boilerplate like EEO, immigration sponsorship, etc. Remove those. We must preserve all substantive information; do not invent or summarize away detail. So we need to keep all details from sections we include. Let's extract content. Company overview: maybe we can include under About the role? But the instruction says About the role heading for section headings; we could include company overview there as part of role description. However the "About SecurityScorecard" is not about the role but about company. Could we omit it? The instruction says preserve ALL substantive information; do not invent or summarize away detail. If we omit company overview, we lose substantive info. So we need to include it somewhere. Perhaps we can include it under About the role heading as a preamble. That seems acceptable. Thus About the role section will contain both company overview and role description. Now responsibilities: list from "What You'll Do". Let's capture each bullet: - 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 Now Required Qualifications list: - 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 Preferred Qualifications list: - 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 Our Tech Stack: description: "Our platform runs on Node.js and TypeScript, with a React Microfrontend Architecture and PostgreSQL and ClickHouse for storage. We use Kafka for event streaming, and our infrastructure runs on AWS with Kubernetes, Terraform, Helm, and ArgoCD. We are actively expanding our AI/ML infrastructure. You do not need to have used every tool here, but you should be comfortable reasoning across a stack of this kind and making principled architectural trade-offs within it." We can turn that into a list of skills: Node.js, TypeScript, React Microfrontend, PostgreSQL, ClickHouse, Kafka, AWS, Kubernetes, Terraform, Helm, ArgoCD, AI/ML infrastructure. We'll put under Skills heading. Benefits: "Specific to each country, we offer a competitive salary, stock options, Health benefits, and unlimited PTO, parental leave, tuition reimbursements, and much more!" We'll keep that. Pay: "The estimated total compensation range for this position is $270,000 - $330,000 (base plus bonus). Actual compensation for the position is based on a variety of factors, including, but not limited to affordability, skills, qualifications and experience, and may vary from the range. In addition to base salary, employees may also be eligible for annual performance-based incentive compensation awards and equity, among other company benefits." We'll include that. Now we must not include boilerplate like EEO, immigration sponsorship, etc. Remove those. Now produce HTML fragment with headings

    and content. We need to ensure we don't repeat job title as heading. Not needed. We'll output:

    About the role

    SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated, operating in 64 countries. Founded in 2013 by security and risk experts Dr. Alex Yampolskiy and Sam Kassoumeh and funded by world-class investors, SecurityScorecard’s patented rating technology is used by over 25,000 organizations for self-monitoring, third-party risk management, board reporting, and cyber insurance underwriting; making all organizations more resilient by allowing them to easily find and fix cybersecurity risks across their digital footprint. Headquartered in New York City, our culture has been recognized by Inc Magazine as a "Best Workplace,” by Crain’s NY as a "Best Places to Work in NYC," and as one of the 10 hottest SaaS startups in New York for two years in a row. Most recently, SecurityScorecard was named to Fast Company’s annual list of the World’s Most Innovative Companies for 2023 and to the Achievers 50 Most Engaged Workplaces in 2023 award recognizing “forward-thinking employers for their unwavering commitment to employee engagement.” SecurityScorecard is proud to be funded by world-class investors including Silver Lake Waterman, Moody’s, Sequoia Capital, GV and Riverwood Capital.

    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, 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.

    Then Responsibilities:

    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