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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) CS background preferred (or equivalent demonstrated capability). Strong signal from top engineering programs in India or an equivalent track record (exceptional projects / OSS / internships that clearly show you can perform). How You Work (This Matters) You’ll do well here if you: default to ownership: you don’t wait to be told every step can move fast without being sloppy enjoy ambiguity and turn it into clear execution treat quality as an engineering problem: tests, evals, instrumentation, iteration Why Join Fermi? Build core AI product (not demos) that students use daily. Massive scope for learning: agents, evals, multimodal tutoring, production hardening. Small team, high trust, direct impact—your code ships and matters. Work with a team that cares about craft, not just hype. This is not for you If… You want remote/hybrid (this is onsite Bangalore only). You prefer narrow tickets and minimal ambiguity. You’re not excited about production engineering (monitoring, reliability, cost, latency). You’re mainly looking for a “research-only” role.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.