Note: The job is a remote job and is open to candidates in USA. Sapient Search is building the operating system for live event ticketing at scale. They are seeking a Senior Full Stack Backend Engineer who deeply understands their work and holds themselves and their teammates to rigorous engineering standards.
Responsibilities
- Design and build backend services within a Domain-Driven Design architecture (Express, TypeORM, BullMQ, Tsyringe, Zod), from domain entities to HTTP handlers to async workers
- Own complete features end-to-end: schema design, migrations, API contract, business logic, serialization, test coverage, and observability
- Build and iterate on AI-assisted tooling that meaningfully accelerates the team, including prompt pipelines, code generation workflows, and AI-powered data processing, while maintaining full engineering ownership of what ships
- Contribute to distributed systems concerns: event outbox reliability, queue worker durability (BullMQ/Redis), OpenSearch indexing, and AWS-native integrations (S3, SNS, SQS, Lambda)
- Contribute to responsible AI adoption: defining patterns for where AI tooling belongs in the stack, how to validate its output, and where human judgment is non-negotiable
- Participate in technical design, code review, and on-call, raising the bar for the team around engineering standards
Skills
- 5+ years with Node.js and TypeScript in production environments
- Strong fundamentals in relational database design (PostgreSQL): indexing, constraints, migrations, query optimization, replication
- Hands-on experience with async job processing (BullMQ, Sidekiq, Celery, or equivalent) and event-driven architecture
- Comfort with DDD or clean architecture patterns, knowing the difference between an entity, a service, and an infrastructure concern
- Redis fluency: caching patterns, pub/sub, queue-backed workflows
- Strong unit and integration testing with Jest, writing tests that verify real behavior and hold up under refactors
- Experience building real AI-powered features: prompt pipelines, LLM API integrations (OpenAI, Anthropic, Bedrock, etc.), and RAG systems
- Ability to evaluate AI output critically, knowing when a model is hallucinating, when a generated test is vacuous, and when a suggestion will cause a production incident or lack the ability to scale with the platform
- Understanding of AI engineering tradeoffs: latency, cost, reliability, correctness, and how to design around them
- Clear opinions on where AI belongs in a software system and where it doesn't
- You write tests that actually test something, colocated, integration-level, and factory-backed
- You can read and reason about a migration, a query plan, or a serializer without needing a prompt
- You've worked in a codebase with real constraints: multi-tenant auth, RBAC, zero-downtime deployments, production databases you can't just roll back
- You have a point of view on code quality and aren't shy about expressing it in review
- Prior work in high-throughput transactional systems (POS, payments, ticketing, e-commerce)
Benefits
- 10% bonus
- Fulltime benefits
- 100% Remote
Company Overview