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Open nowPosted 390 days ago

AI Engineer (Founding Engineer – AI)

gradientflo19 open roles

Pay
$24,000 – $32,000 a yearly
Where
Hyderābād, India
Work mode
On site
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Your applicationOpen nowAI Engineer (Founding Engineer – AI)gradientflo · Hyderābād, India

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  5. 35.5%30 days
  6. 45.1%60 days
This job: posted 390 days ago

The posting

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%)
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