AWS Serverless Architect

• Solution Architect - owns end-to-end system architecture; you align AWS-specific patterns within that vision.
• AI Platform Engineer - builds the code intelligence pipeline; you validate extracted patterns and provide feedback.
• Software Development Engineers (SDEs) - Implement features and infrastructure across the full stack using AI coding agents guided by your validated patterns.

Responsibilities

• Define and maintain serverless reference architectures for Lambda, streaming workflows, data access patterns, and API design.
• Provide architectural guidance and reviews across all SDLC phases - design, implementation, code review, deployment, production.
• Own architectural oversight of the full polyglot stack - TypeScript Lambdas, PHP backend, Angular frontend, MongoDB, DynamoDB, OpenSearch.
• Define and govern IaC standards - module structure, naming, tagging, CDK/Terraform configuration review. SDEs author IaC with agent assistance; you review and enforce standards.
• Review and validate patterns extracted by the code intelligence pipeline. Classify as canonical, legacy, or requiring correction.
• Provide structured feedback to AI Platform Engineer to improve extraction accuracy and relevance.
• Validate Lambda design - concurrency, retries, idempotency, failure handling, cost efficiency.
• Own Kinesis-based and event-driven data flow guidance - data pipeline design, ordering guarantees, replay, dead-letter handling, integration points across Lambda, PHP, and Angular layers.
• Review agent-generated code from SDEs for architectural consistency - your reviews close the loop between patterns defined, patterns served to agents, and patterns actually implemented.
• Document architectural decisions, constraints, and trade-offs for team consumption and as input to the code intelligence platform.

• 15+ years software engineering, 6+ years focused on AWS serverless and distributed systems.
• Deep expertise in Lambda-centric serverless architectures at scale.
• Event-driven, streaming, and data pipeline architecture - data flow design, ordering, replay, dead-letter handling, streaming-vs-batch trade-offs. Hands-on with Kinesis, SQS, EventBridge, or equivalent.
• TypeScript / Node.js - sufficient depth to review Lambda design, conduct code reviews, and assess agent-generated implementation patterns.
• DynamoDB - data modelling at scale, single-table design, GSI strategies, access pattern optimization. Non-negotiable.
• Document database experience - MongoDB, Couchbase, DocumentDB, or equivalent. Document modelling, query performance, aggregation patterns, polyglot persistence with DynamoDB. MongoDB preferred but not required.
• OpenSearch / Elasticsearch - index design, search patterns, analytics workloads, scaling.
• IaC (CDK / Terraform) - define standards, review configurations, enforce best practices. Heavy authoring done by SDEs with agent assistance.
• Architectural governance across the full SDLC - design reviews, code reviews, production readiness.
• Ability to reason about architecture from existing code, not just greenfield.

Secondary Skills

• PHP - architectural-level understanding of legacy backend integration with Lambda, data stores, and frontend. Deep PHP expertise not required.
• Angular - frontend-to-backend integration patterns, API consumption, serverless API interaction.
• Experience defining architectural standards across multiple teams.

Good to Have

• Legacy-modern hybrid systems (PHP + serverless coexistence).
• Developer tooling or internal platform team exposure.
• Prior collaboration with AI/ML or data platform engineers.
• Understanding of how patterns are consumed by AI systems (RAG, agents, templates).
• Experience reviewing AI-generated code at scale.