MarvelX is building highly specialized AI agents for the most complex, heavily regulated industries. Think insurance claims, compliance, support. Automated by AI that actually works in the real world. The opportunity is massive, and we’re moving fast.

We’re looking for a AI Engineer to help us push the boundaries of what’s possible with AI in production. Someone who thrives at the intersection of cutting-edge research and shipping real products. Someone who can turn ideas into working systems, fast. You’ll work directly with the founders and tech lead to design, build, and deploy AI agents that solve mission-critical problems for our clients.

This isn’t a typical engineering role. It’s an early seat on the rocket ship.

If that sounds like your kind of challenge, let’s talk.

As a founding AI Engineer, you’ll be at the core of MarvelX’s product and tech evolution. You’ll take ownership of designing and building agentic systems that can handle high-stakes, high-complexity workflows. You’ll explore the edge of LLMs, reasoning engines, and orchestration frameworks, and bring them to life in production environments where reliability, compliance, and explainability matter.

• Build and deploy AI agents that handle real-world workflows in insurance, banking and other financial services domains.
• Experiment with LLMs, retrieval-augmented generation (RAG), tool-use, and multi-agent orchestration to push performance.
• Design pipelines that ensure explainability, traceability, and auditability for every decision the AI makes.
• Collaborate directly with product, design, and GTM to rapidly iterate on customer feedback.
• Optimize models and systems for production: latency, accuracy, scalability, and security.
• Stay ahead of the curve: explore new architectures, open-source tools, and research to keep MarvelX at the frontier.

• 2–4 years of hands-on experience building AI/ML systems (startups, scale-ups, or research labs).
• Strong background in Python, ML frameworks (PyTorch, TensorFlow), and modern AI stacks (OpenAI APIs, vector DBs, etc.).
• Experience with LLMs and agentic architectures - you’ve built with them, broken them, and made them better.
• Solid understanding of data engineering: pipelines, preprocessing, and deploying models in production.
• Proactive, scrappy, and able to ship fast without compromising on quality.
• Excited by solving hard problems where there’s no clear playbook - and you create the playbook.
• Bonus: exposure to compliance, fintech, or other regulated domains where explainability is critical.