Principal Software & AI Architect, Blue Engines

Application close date:

Applications will be accepted on an ongoing basis until the requisition is closed.

At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We’re working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight!  

This role is part of the Blue Origin Engines business unit, where our focus is the design, development, manufacturing, and testing of engines and propulsion systems. Built for multiple uses, our family of engines is powering the next generation of rockets for commercial, civil, national security, and human spaceflight.

Join our passionate and accomplished team of experts. You will support the Blue Engines business unit by delivering top-notch rocket engines. You'll develop software and AI driven solutions that support our program delivery and enables our business operating system. Your expertise in designing, developing, architecting enterprise software will be critical to our success.

You will split focus between hands‑on product/SaaS solution architecture (~70%) and business unit level (BU)/enterprise guardrails (~30%).

Key Responsibilities

Product & SaaS Solution Architecture (near‑term delivery)

• Own end‑to‑end architecture for the AI application: domain model, APIs, UX/backend integration, data flows, and cloud/Kubernetes topology.
• Define and implement multi-tenancy and isolation strategy (pooled vs. siloed vs. VPC), regionalization, and tenant‑aware observability/support.
• Establish SLAs/SLOs, rate limits/quotas, HA/DR, and resilience testing (chaos/failover); drive SLO/error‑budget practices.
• Build commercialization enablers: SSO (SAML/OIDC), SCIM provisioning, fine‑grained RBAC/ABAC/DBAC, a zero-trust architected entitlement service, metering/billing events, feature tiering, API versioning, webhooks/extension points.
• Stand up ML Ops: data pipelines, feature store, model registry, CI/CD for ML, offline/online eval harness, model rollback/kill switches, human‑in‑the‑loop workflows.
• Security & privacy by design: threat modeling (including AI‑specific risks), secrets/KMS, encryption at rest/in transit, audit logging, data retention/deletion, PII handling, secure SDLC controls (SAST/DAST/SBOM).
• Enterprise Architecture & Guardrails (BU‑level)
• Define BU architecture principles, capability map, and target state; publish reference architectures and an approved tech catalog.
• Set identity and integration standards (API/eventing), data governance (lineage, quality, residency), and IaC patterns; create “paved roads” (golden templates for services, pipelines, observability, security checks, w/ auditability).
• Spin‑out readiness: design boundary APIs with the parent company, plan data partitioning and key management, enforce cost isolation/chargeback, and ensure infrastructure portability with IaC.
• Establish lightweight governance (Architecture Decision Records (ADRs), design reviews, fast exceptions) that enables speed while managing risk.

Leadership & Collaboration

• Operate hands‑on with engineers and product leaders.
• Mentor & support engineers on the team.
• Communicate architecture decisions, trade‑offs, and risk clearly to executives, cross‑functional stakeholders, and customers; lead technical sessions with design partners.

Minimum Qualifications

• Minimum Bachelor's ideally Master's degree in Computer Science with 10+ years in software architecture/engineering, including 5+ years leading architecture for customer‑facing SaaS products.
• Strong familiarity with AI and ML concepts
• Demonstrated experience shipping multi‑tenant (or VPC‑isolated) SaaS with defined SLAs and business customers.
• Deep cloud/platform skills: containers/Kubernetes, IaC (e.g., Terraform), networking, CI/CD, observability, performance/cost optimization.
• Security/compliance expertise: threat modeling, secure SDLC controls (SAST/DAST/SBOM), identity (OIDC/SAML) and provisioning (SCIM), audit logging; SOC 2 readiness or operation.
• Data and AI/ML systems: data pipelines/governance, model lifecycle and MLOps, evaluation/guardrails, responsible AI practices.
• Product mindset: understands packaging/entitlements, usage metering, versioning/compatibility, and impact on margins and customer experience.
• Excellent communication and influence across executives, customers, and engineers; ability to create clarity and drive decisions in ambiguity.