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: