Backend Performance & Systems Engineering (Project)

## About the role - Product: Agentic AI tutoring platform - Company: Elumai - Stage: Pre-launch, preparing for general availability - Engagement: Fixed-price project. Budget to be proposed. - Duration: Approximately 10-12 weeks. - Location: Remote. Americas time zones preferred. - Stack: Python, FastAPI, PostgreSQL, Google Cloud Platform ## About Elumai Elumai is an agentic AI tutoring platform. The system pairs specialized expert models with per-learner adaptation that captures how each learner actually thinks, not just what they get right. The backend is mature and already in production; we are preparing for general availability and investing in the performance and infrastructure that will carry us there. This is a hands-on, high-autonomy role suited to someone who prefers a small, experienced team to a large organization, and who is comfortable working in an actively evolving codebase as we approach general availability. We are a small team working without intermediary management so you would work directly with the founder and frontend engineer. ## Project overview This is a finite engagement to deliver a defined set of performance and systems improvements across the Elumai backend. The project spans five workstreams: queue and background worker architecture, database performance, core subsystem performance, API contract refinement, and edge-case latency and production readiness. Each workstream has named deliverables and acceptance criteria. The engagement is for incremental improvement to harden subsystems and refine architecture progressively, not a single large rewrite. The engagement ends when those deliverables are met. ## Scope ### Queue and background worker architecture - Select and deploy a queue primitive appropriate to our stack (Cloud Tasks, Pub/Sub, Celery, Temporal, or equivalent). - Migrate non-response compute off the critical path with durable retry semantics. - Define retry, failure, and observability behavior for all queued work. - Produce a design document and operational runbook. ### Database performance - PostgreSQL query plan analysis and index design on the highest-impact endpoints. - Connection pooling and query shaping. - Pgvector tuning for our retrieval workloads. - Establish and document an eager-vs-lazy loading strategy across the pipeline rather than leaving it to per-endpoint convention. ### Core subsystem performance - Implement time-budgeted execution with graceful degradation under load on core retrieval and computation paths. - Measure baseline and improved performance. - Document behavior, budgets, and fallback paths. ### API contract refinement - Partner with our frontend engineer to define response shapes, streaming envelopes, and error semantics. - Contracts should be driven by the needs of the user interface, not the convenience of the backend. - Document the contracts so they can be extended after the engagement ends. ### Edge-case latency and production readiness - Bring latency in edge-case paths closer to parity with the primary path through pre-warming, parallel speculative execution