Lead the design and implementation of backend services and APIs for key product areas, balancing delivery speed with reliability, scalability, and maintainability.
Partner with Product and Design to clarify requirements, propose solutions, estimate effort, and deliver milestones with clear success metrics.
Build and operate applied AI features in production (e.g., LLM-backed workflows, evaluation/guardrails, retrieval patterns, experimentation, and monitoring) with a focus on quality, safety, and cost.
Contribute to architecture decisions through RFCs/design docs, proposing pragmatic patterns for service boundaries, data models, async workflows, caching, and migrations.
Write high-quality code and raise the bar through code reviews, pairing, and sharing best practices; mentor junior engineers as needed.
Improve operational health for the systems you own: observability, performance tuning, reliability improvements, and on-call outcomes.
Participate in an on-call rotation and help debug/resolve production issues; contribute to post-incident learnings and follow-ups.
Break down product requirements into well-scoped tickets and workstreams, coordinating with teammates to deliver predictably.

6–10+ years building and operating production backend systems (consumer and/or B2B SaaS), including scaling, reliability, and distributed systems exposure.
Strong experience with AWS (EC2/Fargate/Lambda/Queues), Terraform (or comparable IaC), Redis, relational databases, and SQL (Postgres).
Strong experience with JavaScript/Node.js, TypeScript, and NestJS (or comparable modern frameworks).
Experience with Docker/containerization, CI/CD, and production deployment patterns.
Proven ability to design and ship scalable systems, and to make sound tradeoffs across APIs, data modeling, async patterns, caching, and migrations.
Strong instrumentation/observability skills (metrics/logging/tracing), performance debugging, and cost awareness (e.g., Datadog).
Clear communicator who collaborates well with technical and non-technical partners.
Comfortable in startup environments: ownership, pragmatism, and delivery under uncertainty.
Applied AI / Generative AI (Must Have)
Practical experience shipping AI-enabled features into production, including LLM-backed workflows, retrieval patterns (RAG, embeddings, vector databases), evaluation and guardrails, and monitoring for quality, latency, cost, and model drift.
Nice to Have: Experience building internal platforms or developer tooling; background in edtech or learning analytics; familiarity with modern privacy and security best practices (PII handling, least privilege, secure-by-default).

Senior Software Developer, Backend

Key skills