AI Engineer

About Fermi

Fermi is an early-stage AI tutoring startup, an initiative of Meraki Labs, dedicated to revolutionizing education globally. Our mission is to democratize world-class tutoring by making it accessible to every student through advanced AI. We are a dynamic and lean team of educators, designers, and engineers, united by a strong conviction that thoughtful design and cutting-edge AI can profoundly transform the learning experience.

Led by experienced founders, including Mukesh Bansal (Founder – Myntra, CureFit, Nurix) and Peeyush Ranjan (VP-Google, CTO-Flipkart, Airbnb), we are building a product that aims to reshape how millions of students learn—turning academic challenges into accomplishments. We operate out of Bangalore, India, fostering a high-velocity environment that prioritizes impact, ownership, and direct collaboration to define and build the future of AI in education.

Role Overview

We’re hiring an AI Engineer who is a problem-solver first: someone who can ship, debug, and iterate fast. You’ll help build the core AI workflows powering our tutoring product—especially agentic tutoring systems—and harden them into production-grade, scalable, observable systems.

This role is intentionally not for everyone. If you want tight scope, predictable tasks, or “only research / only backend,” this won’t fit. If you like building real systems end-to-end and seeing your work hit production quickly, you’ll love it.

What You’ll Do

• Build and ship core AI workflows for our tutoring app:

  • agentic tutoring flows (hinting, step-by-step guidance, misconception detection)

  • answer evaluation / grading and feedback loops

  • retrieval + grounding (content ingestion, chunking, embedding, re-ranking)

  • personalization (student memory, progress signals, difficulty adaptation)

  • multimodal pipelines (images/diagrams; bonus if you’ve touched voice)

  Turn prototypes into robust production systems

  • latency + cost optimization (caching, batching, streaming, fallbacks)

  • eval-driven iteration (offline test sets, regression checks, quality gates)

  • observability (traces, logs, metrics, prompt/version tracking)

  • reliability + safety (guardrails, refusal behavior, policy/age-appropriate output)

  Own features end-to-end

  • from rough PRD → implementation → deployment → monitoring → iteration

  Collaborate closely with product/design/education to translate learning goals into AI behavior.

  What We’re Looking For (Must-have Signals)

  Core engineering

  • 1 to 5 years of strong Python fundamentals; you can write clean code and also “hack” when needed.

  • Comfortable building services with FastAPI/Flask, writing APIs, and debugging production issues.

  • Practical understanding of Docker, local dev workflows, and basic deployment concepts.

  • Good CS foundations (data structures, basic systems thinking, debugging).

  AI engineering readiness

  • Hands-on experience with at least one:

    • OpenAI SDK (or similar), LangChain, Haystack (or comparable LLM framework)

    You understand the difference between

    • prompting vs tooling vs retrieval vs agents vs evals

    You’ve built something real

    • internships, substantial course projects, shipped side projects, research engineering, or open-source contributions.

    Pedigree / proof of work (we care about evidence, not labels)

    The rest of this description is on the employer’s own page.