Agent Engineer | AI

Role OverviewAI Agent Engineer / Applied AI Engineer (100% Hands-On Coding)Pay: 45 LPA (20 Base) · Remote (India-based) · US Hours · 5 days/weekNote: You will own production AI systems end-to-end: agent orchestration, prompt systems, evaluations, product POCs, customer deliverables, and production feature launches. This role is designed for someone with a strong owner mindset who can work independently, perform under pressure, and deliver on tight deadlines with almost no support. If you’re a growth-minded builder, indie hacker, or cracked engineer who likes shipping fast and taking full ownership, please apply.About UsWe’re an AI agentic learning startup based in San Francisco and Toronto, transforming how the world learns. Our platform doesn’t just stream videos—it talks back, remembers, questions, adapts, and drives 10x better retention. Picture a real-time Zoom session with your smartest teacher—except it’s fully powered by AI.We’re backed by top-tier investors and built by a team from IIT, Microsoft, Amazon, Bain, and Goldman Sachs. If you're excited to shape zero-to-one AI products, ship fast, and build systems learners love, you’ll fit right in.What We’re Looking For2–5 years of hands-on coding experience in Python and/or JavaScript/TypeScript, with strong ability to independently debug, ship, and maintain production systems.Deep practical experience building with LLMs, AI agents, tool use, workflows, orchestration systems, prompt pipelines, and evaluations.Strong understanding of agent reliability: prompt design, context management, structured outputs, tool calling, memory, retries, fallbacks, guardrails, and evals.Experience using or building with agent skills / tool abstractions / workflow systems extensively in production.Comfortable owning large model orchestration stacks, including routing across models/providers, latency/cost tradeoffs, failure handling, and observability.Strong product instincts—you do not just write code, you build systems that create user value.Proven ability to create and ship quickly—you’ve built prototypes, launched features, improved workflows, and iterated fast based on real usage.A track record of quantifiable outcomes—better reliability, reduced latency/cost, higher conversion, improved retention, or faster delivery.Nice to have: published AI/ML research, strong open-source contributions, a GitHub with meaningful traction/stars, or experience at a high-functioning startup / Series A environment.Available to join soon. We’re scaling quickly.What You’ll DoBuild and maintain AI agents in production: design, ship, and improve agent workflows that power learning experiences and internal operations.Own orchestration end-to-end: prompts, tools, skills, routing logic, memory, evals, monitoring, and fallback strategies.Design robust prompting systems: create and maintain prompt libraries, structured instructions, reusable patterns, and testing workflows.Run evaluations continuously: build eval datasets, define success metrics, analyze failures, and improve agent quality over time.Ship product features directly: work across backend, frontend, and AI layers to launch user-facing functionality fast.Debug everything: model failures, flaky agent behavior, broken tool usage, poor outputs, UI bugs, workflow bottlenecks, or edge-case regressions.Improve speed, reliability, and cost across our AI stack.Collaborate tightly with product, design, and learning teams to build experiences that feel intelligent, polished, and human.Move fast with ownership: if something is broken, unclear, or missing, you fix it.Who You AreAn engineer first—you are deeply hands-on and love coding.A builder/hacker who can go from idea to shipped system without waiting for perfect specs.Someone who is obsessed with AI quality, not just demos—reliability, evals, edge cases, and production behavior matter to you.A growth-minded operator who cares about shipping things that actually move user and business metrics.Comfortable doing whatever the moment requires: prompt tuning, writing backend logic, fixing frontend issues, reviewing outputs, improving evals, or patching workflows.A gritty executor who thrives in a high-speed, high-context startup environment.Low ego, high ownership, and highly autonomous.Strong Signal ProfilesYou’ll likely be a strong fit if you are one or more of the following:An AI engineer / agent engineer who has built real LLM systems in production.An indie hacker or growth hacker who has shipped scrappy, high-leverage products quickly.Someone who has worked at a strong early-stage startup and is used to chaos, speed, and ownership.Someone with a research bent—for example, published work in AI/ML or deep technical curiosity beyond surface-level prompting.An engineer with a strong GitHub/open-source track record or visible technical credibility online.Our Culture & ValuesCustomer Obsession: Everything you ship must make learners go, “Whoa.”Be an Owner: You’re accountable from idea to impact.Superpumped: High energy, high agency—we solve real problems with heart.Bias Toward Output: Perfect is the enemy of shipped.Nothing But Excellence: If it’s buggy, slow, or unreliable, it doesn’t go live.Perks & BenefitsCompetitive Pay: Base salary + ESOPs + monthly bonuses tied to outcomes.Work-Life Balance: Health benefits, gym perks, paid time off, and birthday leave.Cutting-Edge AI Work: Get hands-on with LLM orchestration, evaluations, real-time AI systems, prompt engineering, and agent workflows at production scale.Massive Ownership: Your fingerprints will be on the architecture, the product, and the company’s core AI systems.If you’re the kind of engineer who can architect it, code it, evaluate it, debug it, and scale it yourself—we’d love to talk.

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