Compensation: $115k – $170k • No equity
Summary
We're hiring a senior Agentic AI Engineer on a project-based engineer to audit, architect, and harden our production personalization engine. You'll work directly with our Head of Product and engineering team to take working prototypes to production-ready quality before November 2026.
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:
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Architecture audit
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
-
RAG / retrieval design review
- 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
- Prompt + eval system
- Supervisor routing prompts
- Vertical-agent prompts (Courses, Jobs, Comms)
- Structured-output validation
- Regression eval set design + CI integration
- Failure-mode catalog
- Cost discipline
- 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
First 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
- 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