AI Engineer (Founding Engineer – AI)

Role Overview

You will be the intelligence architect of Vibecoderz. As the AI Engineer, you’ll design, implement, and optimize the multi-agent orchestration system that powers the TutorAgent, PlannerAgent, ScreenPerceptionAgent, and CodeAgent.

This is not a research-only role. It’s applied AI at scale: integrating Gemini models, Pub/Sub protocols, LangSmith evals, and Neo4j graphs into a production-grade learning platform. You’ll work closely with Backend, Prompt, and Frontend engineers to ensure AI capabilities feel seamless, fast, and trustworthy for 150M+ developers worldwide.

You’ll use Linear for task management, Notion for specs and experiments, and GitHub for code reviews, making the AI system’s evolution transparent and collaborative.

Key Responsibilities

1. Multi-Agent Orchestration - Implement TutorAgent and sub-agents using the Google Agent Development Kit (ADK), ensuring modularity and reliability.

2. Model Integration - Integrate Gemini Pro, Gemini Vision, and Gemini Live API for multimodal reasoning (text, vision, voice).

3. Communication Protocols - Design and maintain Agent-to-Agent (A2A) communication via Google Pub/Sub. Guarantee low-latency, async messaging.

4. Context & Memory Systems - Build working memory in Redis, long-term user data in Firestore, and Developer Graph in Neo4j to support adaptive tutoring.

5. Artifact Generation - Collaborate with Prompt Engineers to refine artifact workflows: slides, quizzes, code snippets, and runnable mini-apps.

6. Evaluation & Guardrails - Build evaluation pipelines in LangSmith/Langfuse to test prompt stability, reduce hallucinations, and monitor drift.

7. RAG & Vibe Browser - Integrate retrieval-augmented generation (RAG) pipelines with GitHub docs, StackOverflow, and MDN for contextual support.

8. Adaptive Learning Loops - Implement performance-tracking systems that adapt course flow based on learner progress and quiz outcomes.

9. Scaling & Optimization - Benchmark and optimize model selection via hybrid routers (Gemini Flash for speed, Pro/Claude for depth).

10. Security & Safety - Implement guardrails against unsafe generations, prompt injection, and biased outputs.

11. Cross-Team Collaboration - Translate PM requirements into AI system designs, coordinate with Backend for APIs, and FE for real-time outputs.

Success Metrics

90 Days (Probation):

• TutorAgent and PlannerAgent integrated with Gemini Pro + Pub/Sub messaging.

• Redis working memory and Firestore user schema live.

• LangSmith evaluation pipeline created with baseline metrics (<15% hallucination rate).

12 Months:

• Orchestrate 10+ specialized agents in production.

• Reduce TutorAgent response latency <3s end-to-end.

• Adaptive learning system live with >40% improvement in learner retention.

• AI drift monitoring and guardrails automated in CI/CD.

Must-Haves

• 10+ years in applied AI/ML engineering.

• Expertise in LLM orchestration frameworks (LangChain, CrewAI, ADK).

• Deep experience with multimodal model integration (text, voice, vision).

• Strong knowledge of messaging systems (Pub/Sub, Kafka, or similar).

• Proven delivery of AI-first products in production environments.

Nice-to-Haves

• Research background in NLP, RLHF, or agentic AI systems.

• Contributions to open-source AI frameworks.

• Prior work on developer-focused AI products.

• Startup/founding engineer experience.

Tech Stack Visibility

• Core AI: Google ADK, Gemini Pro, Gemini Vision, Gemini Live API

• Communication: Google Cloud Pub/Sub

• Memory: Redis (working), Firestore (archival), Neo4j Aura (Developer Graph)

• Eval & Prompting: LangSmith, Langfuse, hybrid model router

• Infra: Cloud Run, API Gateway, GitHub Actions

• Tools: Linear (execution), Notion (PRDs/experiments), GitHub (repos)

Assessment

Objective: Validate ability to design and implement a production-ready multi-agent system.

Challenge (Candidate PoC):

1. Build a TutorAgent that:

   • Takes input: “Teach me React Hooks.”

   • Delegates to sub-agents:

     • CurriculumAgent → outline

     • ContentAgent → lessons

     • CodeAgent → runnable code snippet

     • QuizAgent → quiz JSON

   • Stores all results in Firestore + Developer Graph (Neo4j).

   • Communicates via Pub/Sub.

2. Add a Learning Adaptation Loop:

   • If quiz score <60%, regenerate lesson with simplified examples.

3. Evaluation & Guardrails:

   • Set up LangSmith eval pipeline with at least 20 golden prompts.

   • Implement guardrail filter to block unsafe outputs.

4. Performance Targets:

   • TutorAgent orchestration end-to-end <3s.

   • Sub-agent response <1.5s each.

Deliverables:

• Multi-agent orchestration codebase.

• Firestore + Neo4j schema examples.

• Evaluation report (accuracy, latency, hallucination rate).

• GitHub repo with CI integration.

• 5-min Loom demo walkthrough.

Evaluation Criteria:

• Architecture & Orchestration Design (30%)

• Model Integration & Multimodal Handling (20%)

• Evaluation & Guardrails Implementation (20%)

• Performance & Scalability (15%)

• Documentation & Testing (15%)