Senior Agentic AI Engineer

We're hiring a senior Agentic AI Engineer on a project-based contract to audit and harden our production personalization engine. You'll work directly with our Head of Product (Chris) and engineering team to take working prototypes to production-ready quality before November 2026. This is a senior, project-based engagement (not full-time). Estimated 30–60 hours per month, ~3 months, with the possibility of extension. We're building a career-intelligence and upskilling platform serving learners across MENA and Africa. We deliver outcomes — completed cohorts, secured placements, career progression — for government training contracts, university partnerships, and large-employer partnerships. What you'll do: We've prototyped a personalization engine on top of our new Learn app. The basic framework exists to validate the concept; we want a senior engineer to make it production-grade. Specifically: 1. Architecture audit (Weeks 1–3) Review the personalization engine end-to-end: - Zone 1 — Surfaces: homepage canvas, in-course chat, events / jobs / comms cards - Zone 2 — Agents: LangGraph supervisor + vertical agents (Courses, Events, Jobs, Comms) - Zone 3 — Backends: MongoDB Atlas vector store, course content + transcript ingestion, employer pipeline, PostHog telemetry - Zone 4 — Self-improvement loop: scoring agent → user.md → tuned routing Output: written assessment of what's load-bearing, what scales, what needs to change before COP32 onboarding (~10K learners, Nov 2026). 2. RAG / retrieval design review (Weeks 2–5) - Chunking strategy for video transcripts + Markdown lessons - Hybrid retrieval (dense + sparse) recommendations - Reranking strategy - Per-user scope enforcement (no cross-tenant leakage) - Multilingual retrieval — Arabic + English minimum; Arabic word-error-rate is real - Vector store choice review — MongoDB Atlas today; pgvector under evaluation 3. Prompt + eval system (Weeks 4–8) - Supervisor routing prompts - Vertical-agent prompts (Courses, Jobs, Comms) - Structured-output validation - Regression eval set design + CI integration - Failure-mode catalog 4. Cost discipline (Weeks 6–10) - Per-feature + per-organization token budgets with enforcement (we bill at org level) - Cache strategy (we already cache canvas cards by content version) Multi-tier model routing — frontier (Sonnet / GPT-4o) for paid cohorts, mid-tier for general learners, cheap-tier or self-hosted for unverified Anti-abuse limits — topical-relevance classification, per-user daily caps Cost reporting to PostHog dashboard Our current stack - LLMs: OpenAI + Anthropic (multi-provider posture) - Orchestration: LangChain.js + LangGraph (supervisor + sub-agent pattern) - Vector store: MongoDB Atlas (pgvector swap under evaluation) - Backend: Node.js, Express, BullMQ workers, MySQL (Aurora) - Frontend: Next.js 15 App Router, React, Tailwind - Eval / observability: PostHog (in-flight); LangSmith or Helicone under evaluation What success looks like After 3 months we should have: - Architecture assessment - Working RAG/retrieval pass with documented quality metrics on a fixture eval set - Production-ready prompt + eval pipeline in CI - Adaptive AI framework that will improve based on learners' interactions - Scaffolding for evaluations / quality control - Cost projection for ~10K learners with cap + cache + tier strategy locked Who you are Required: - Built production agentic systems before — not just chat wrappers around an LLM API - Strong production RAG experience — chunking, retrieval quality, eval discipline - Comfortable in JavaScript / TypeScript (Node + Next.js) - LangChain.js / LangGraph experience, or strong opinions on alternatives you can defend - Cost-aware — you've watched LLM bills explode and have systems-level opinions about budgets, caches, multi-tier routing - Able to teach — engineers on the team are learning; you'll be expected to pair, write, and explain Strongly preferred: - Multilingual retrieval (especially Arabic) - Eval framework experience (LangSmith, Helicone, custom) - Vector store experience beyond Mongo (pgvector, Qdrant, Pinecone) - Worked on platforms (not just internal tools) — you've shipped to real users Engagement details Duration: ~3 months starting June 2026 Time commitment: project-scoped; estimate 30–60 hours per month Rate: senior contractor; competitive, scoped per project Location: anywhere — async-first Communication: weekly sync with Chris; ad-hoc Slack with team How to apply Send a short note about a production agentic system you built or significantly improved