AI Systems Engineer (LLM Performance, Cost & Reliability) | Audit → Recommend → Implement

Overview Jules is a mobile AI-powered style and dating photo coach. We analyze outfit photos and dating profile images, score them, and give actionable feedback using LLMs and vision models. The product is live, architected, and thoughtfully built. What we need now is systems-level optimization. We’re looking for a senior engineer to audit, optimize, and harden our LLM infrastructure — reducing latency and cost while improving reliability and consistency — without changing product flows or UX. This is not a greenfield build. This is not prompt polishing. This is a real production system that needs to scale. What You’ll Do Phase 1 Audit all LLM usage across the system: FitCheck (vision) PicReview (vision) Comparison modes Conversational chat Analyze: Latency bottlenecks (user-perceived and backend) Cost per request / feature / user Model usage vs actual requirements Prompt size, retries, determinism, and waste Review existing cost instrumentation and update pricing assumptions Deliverable: A written audit outlining: Current performance & cost profile Clear problem areas Ranked list of optimization opportunities with estimated impact Phase 2 — Optimize & Implement Implement agreed optimizations directly in the codebase, which may include: Multi-model routing (cheap → expensive fallback) Vision + text model rationalization Caching (hash-based, context-based, or result reuse) Async coordination improvements (queues, batching, retries) Prompt minimization and structural refactors (not stylistic rewrites) More accurate cost tracking and reporting Ensure output stability and scoring consistency are preserved Deliverable: Merged code changes Before/after latency and cost comparison Clear documentation of decisions and tradeoffs What You Will Not Do To be explicit: ❌ Redesign product flows, UX, or scoring logic ❌ Rewrite Jules’ persona or tone ❌ “Improve” the product by adding features ❌ Push unnecessary infra churn before instrumentation ❌ Suggest fine-tuning as a first solution Your job is to make the engine faster, cheaper, and more reliable, not change the car. Technical Environment (You’ll Be Working Inside This) Frontend: React Native (Expo, TypeScript) Backend: Node.js + Express Database: MongoDB AI: OpenAI (GPT-4o for vision, GPT-4.1-mini for chat) Infra: Cloudinary (images), Firebase Auth, Segment, Sentry Architecture: Async API calls, structured JSON outputs, prompt routing system Full architecture documentation will be provided on engagement start. What We’re Looking For Required Deep experience optimizing production LLM systems Strong intuition for cost vs latency vs quality tradeoffs Hands-on backend engineering skills (Node.js) Experience with: model routing async systems caching strategies deterministic LLM outputs Nice to Have Vision model experience Experience evaluating multiple inference providers Prior startup or zero-to-scale experience Engagement Details Type: Short-term contract Length: TBD Scope: Audit → Recommend → Implement Potential extension: Yes, based on results Timezone: Flexible, but on Pacific Time Apply tot his job