Principal Architect

Toyota•Plano, TX

About The Position

At TFS, we're building a greenfield, cloud-native engineering platform that will redefine how Toyota Financial Services delivers digital products to millions of customers. We're looking for a Principal Architect — a senior individual contributor at the top of our IC track — who owns the technical coherence of the entire platform. This is not a management role and it's not an advisory role. It's for the engineer who can see across every layer of the system — application, data, AI/ML, and infrastructure — make binding architectural decisions and then get in the code to validate them. You'll be the connective tissue between engineering teams, setting direction across the stack, shaping standards that last, and serving as the person engineering leadership trusts to say, "this is how we build it."

Requirements

Bachelor's degree in computer science or related field, or equivalent practical experience
10+ years of software engineering experience, with at least 5 years of operating as a hands-on architect or principal-level IC on AWS-based platforms
Proven track record of owning full-platform architecture decisions in production — not just advising, but being accountable for the outcomes
Deep, hands-on expertise with serverless-first AWS architecture , including: Lambda , API Gateway (REST and HTTP), Step Functions , EventBridge , SQS/SNS for compute and async orchestration
DynamoDB and/or Aurora Serverless for data persistence patterns
S3 , CloudFront , and edge patterns for object storage, CDN strategy, and API acceleration
Experience designing multi-account AWS architectures with proper governance: AWS Organizations, Control Tower, Service Control Policies, and cross-account IAM patterns
Strong command of IaC design and governance using AWS CDK, CloudFormation, or Terraform — owning infrastructure design end-to-end, not just reviewing it
Architecting S3-based lakehouse patterns : table formats (Apache Iceberg or Delta Lake), partitioning strategies, and lifecycle management
Hands-on design of event-driven streaming pipelines using Kinesis, MSK/Kafka, or similar, through to analytics with Athena, Redshift, or equivalent
Familiarity with data governance patterns: Lake Formation, data cataloging, access control for sensitive financial data
Practical experience integrating Amazon Bedrock , SageMaker endpoints, or equivalent LLM/ML services into production application architectures
Understanding of agentic flow design — tool calling, memory patterns, human-in-the-loop checkpoints, and failure recovery in multi-step agent workflows
Experience with RAG pipeline architecture — embedding generation, vector store design (OpenSearch, pgvector, or similar), and query-time retrieval patterns
Ability to define architectural standards for LLM reliability: structured outputs, guardrails, observability for model behavior, and prompt engineering governance
Proven ability to design and govern RESTful and GraphQL API contracts at scale — versioning strategies, federation patterns, type-safe tooling (OpenAPI, tRPC, GraphQL Code Generator)
Experience with frontend architecture at platform scale: Next.js on AWS (CloudFront, Lambda@Edge), monorepo tooling (Turborepo/Nx), and how front-end and back-end contracts evolve together
Deep understanding of security architecture : IAM least-privilege design, secrets management (Secrets Manager, SSM Parameter Store), encryption patterns, and network segmentation
Strong experience with distributed observability : CloudWatch, X-Ray, Datadog — defining SLAs, building dashboards, instrumenting services for traceability, and leading incident response
Strong written and verbal communication — you can author a clear RFC, lead a contentious design review, and explain a complex architectural tradeoff to a non-technical executive
Track record of influencing without authority: aligning multiple engineering teams on a shared direction through credibility, evidence, and clear thinking
Experience defining and maintaining engineering standards across an organization — code quality, testing strategies, deployment safety, and architectural review processes.

Nice To Haves

Master's degree in Computer Science or related field
Experience in financial services, banking, or insurance — understanding of compliance, data residency, and auditability requirements
AWS certifications: Solutions Architect Professional, Data Analytics Specialty, or Machine Learning Specialty
Experience with data mesh or data product patterns — decentralized ownership with centralized governance and discoverability
Hands-on experience with responsible AI practices : fairness metrics, bias detection, explainability (SHAP/LIME), and model monitoring
Experience with CI/CD at platform scale — pipeline design, deployment strategies (blue/green, canary), and progressive delivery frameworks
Experience contributing to or defining open standards, internal frameworks, or open-source projects
Familiarity with GraphQL federation for composing distributed services across teams

Responsibilities

Own the end-to-end reference architecture for our engineering platform team — application, data, AI/ML, and infrastructure layers — ensuring coherence across all product domains
Make binding, well-documented architectural decisions across teams; author RFCs, ADRs, and design documents that become the foundation for how we build
Lead platform-wide technical design reviews and architecture forums, driving alignment across engineering leads, engineering managers, and product
Define, evolve, and enforce engineering standards: API design contracts, IaC patterns, security posture, observability baselines, and deployment safety
Identify and resolve systemic risks before they become incidents — performance ceilings, reliability gaps, coupling issues, and architectural drift
Evaluate and recommend new technologies, frameworks, and AWS services; balance innovation with operational maturity and team capability
Contribute with spikes/production-quality code in TypeScript and/or Python — you validate architectural decisions by writing and reviewing the code, not just describing it
Own the design and lead implementation of the most complex, cross-cutting platform components: shared infrastructure modules, API frameworks, data pipeline foundations, and AI/ML integration layers
Lead technical troubleshooting for the hardest distributed system failures — from API latency and data pipeline failures to infrastructure misconfigurations and LLM reliability issues
Review and approve architectural changes across codebases, holding a consistently high bar for quality, testability, observability, and security
Architect the data backbone of the platform: S3-based lakehouse design, event-driven streaming pipelines, data orchestration, and analytical query patterns
Define integration patterns for AI/ML capabilities — how Bedrock agents, SageMaker endpoints, RAG pipelines, and agentic workflows connect to the application and data layers as first-class architectural concerns, not bolt-ons
Establish guardrails for LLM-based systems: structured outputs, prompt engineering standards, observability for model behavior, and patterns for deterministic, production-grade AI features
Partner with the ML/AI engineering team to ensure AI components are observable, testable, and maintainable at production scale
Serve as a technical thought partner to Engineering Managers and the VP of Engineering — translating complex technical tradeoffs into clear, actionable recommendations for both engineering and non-technical stakeholders
Partner with Product and Program Management to assess technical feasibility, shape roadmaps, and surface risks early
Drive the RFC and design review process across the engineering organization, ensuring major decisions have appropriate rigor and broad visibility
Mentor senior and lead engineers — through design reviews, pairing, and written feedback — raising the technical ceiling of the entire org
Contribute to hiring by conducting technical interviews for senior IC and architect-level roles, and helping define what architectural excellence looks like at TFS
Champion a culture of documentation: ADRs, runbooks, design documents, and onboarding guides that transfer knowledge and reduce key-person risk

Benefits

A work environment built on teamwork, flexibility, and respect
Professional growth and development programs to help advance your career, as well as tuition reimbursement
Team Member Vehicle Purchase Discount
Toyota Team Member Lease Vehicle Program (if applicable)
Comprehensive health care and wellness plans for your entire family
Toyota 401(k) Savings Plan featuring a company match, as well as an annual retirement contribution from Toyota regardless of whether you contribute (if applicable)
Paid holidays and paid time off
Referral services related to prenatal services, adoption, childcare, schools and more
Tax Advantaged Accounts (Health Savings Account, Health Care FSA, Dependent Care FSA)
Relocation assistance (if applicable)