Deriv's mission is Trading for Anyone, Anywhere, Anytime. Millions of traders, around the clock. This scale demands AI that works in production, not prototypes that demo well.
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We're already here: AI resolving 65%+ of customer enquiries, writing and reviewing code, processing invoices, and screening candidates. Not experiments. Production systems you'll help extend.
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Why Deriv<\/b>
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Learn by building, not by watching. Here's where you'll do it:
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- Customer experience<\/b>: Building AI that handles conversation, outreach, and lifecycle management.<\/span>
<\/li>- Developer infrastructure<\/b>: Building the systems that build systems (Spec -to -PR, QA automation, security scanning).
<\/li>- Business functions<\/b>: Building the AI that runs Deriv (finance workflows, HR automation).
<\/li><\/ul>Your placement depends on team needs and your interests. You'll likely focus on one area but touch several, with real ownership and support along the way.
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What you'll do<\/b>
<\/div>- Build features that go to production<\/b>: You won't just write scripts; you'll ship code that runs in live environments.
<\/li>- Work across three paradigms<\/b>: You'll learn to combine deterministic systems (code), predictive models (ML), and agentic systems (LLMs).
<\/li>- Learn from failure<\/b>: You'll understand why guardrails matter when a 1% error rate means thousands of wrong decisions.
<\/li>- Pair with experienced engineers<\/b>: You'll own small features end -to -end with guidance from senior mentors.
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Who you are<\/b>
<\/div>- You write code that runs<\/b>: Python or another language you genuinely enjoy. You know syntax is easy; making things work in production is where it gets interesting.
<\/li>- You've touched ML or LLMs<\/b>: Courses, side projects, experiments. Enough to know what you don't know yet.
<\/li>- You deliver reliably<\/b>: You distinguish urgent from important and keep your promises on delivery dates.
<\/li>- You're comfortable being wrong<\/b>: You'll ship code that breaks. That's how you learn—if you can admit it and fix it.
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Tech stack<\/b>
<\/div>- Languages<\/b>: Python, TypeScript
<\/li>- AI/ML<\/b>: OpenAI APIs, Anthropic APIs, LangGraph, Custom ML Pipelines
<\/li>- Infrastructure<\/b>: AWS, PostgreSQL, Redis, Docker, LangFuse
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The honest reality<\/b>
<\/div>This is demanding work. You'll face problems without clear answers. You'll ship code that breaks and fix it under pressure. Some weeks will be frustrating.
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But you'll ship AI that runs—not demos, not prototypes. You'll see your work handling real transactions. And you'll grow fast.
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