AI Engineer - Platform​/MLOps

About hyperexponential (hx)

At hyperexponential, we’re building the AI-powered platform that enables the world’s most critical decisions in a $7 trillion industry, which risks to take, and how to price them. These are the decisions that shape real-world outcomes: whether rockets successfully launch into space, autonomous vehicles make it to market, or communities recover after major storms.

Until now, insurance has been making billion-dollar decisions using outdated tools. We’re changing that. Our platform brings together data, AI, and human expertise to give insurers the fastest path from submission to decision - helping them move faster, act smarter, and take on more risk with confidence.

Backed by a16z, Highland Europe, and Battery Ventures, we’re scaling globally - already trusted by nearly 50 of the world’s largest insurers, with zero churn and billions in premiums flowing through hx.

What began as a single product in one market has rapidly evolved into a multi-product, multi-territory platform powering every stage of pricing and underwriting. AI is at the core of what we do - from building the world’s first domain-specific AI peer programmer for insurance (think Git Hub Copilot with a PhD in actuarial science) to shaping agentic workflows that reinvent how this industry operates.

What makes hx different is the people who build it. Here, impact isn’t tied to title or tenure; it’s defined by the challenges you take on and the discipline you bring. Surrounded by peers who stretch you, you’ll do the best, hardest work of your life in a company engineered to endure.

If that sounds like you, join us in building what comes next.

At hx, AI is central to how we build software and make decisions across the company. From large-scale document intelligence to a domain‑specific AI peer‑programmer used by actuaries and underwriters, hx is redefining how the insurance industry uses generative AI in production.

The AI Platform team provides the platform that makes all of this possible. We design, build, and operate the shared systems that allow teams to train, deploy, evaluate, and monitor AI safely at scale — including model serving, data pipelines, observability, deployment automation, and compute orchestration.

Working closely with product and research teams, we turn fast-moving experimentation into reliable, cost‑efficient, and developer-friendly production systems. Our success is measured in faster releases, stronger model performance in the real world, and AI products that customers can trust with critical decisions.

• Designing and operating scalable AI infrastructure for LLM inference, prompt management, and evaluation pipelines, supporting billions in premium flow.
• Building self‑service tools, SDKs, and APIs that empower product teams to move from prototype to production 30% faster.
• Instrumenting production AI/ML workloads with standardised logging, tracing, and evaluation metrics, increasing observability coverage to 100% of deployed models.
• Implementing intelligent routing, caching, and provider optimisation via the LLM gateway, reducing AI compute costs by up to 25%.
• Driving adoption of shared platform services (LLM gateway, evaluation frameworks, monitoring) to replace bespoke solutions, increasing platform adoption across new AI features.
• Championing developer experience by delivering comprehensive documentation and responsive support, resulting in higher internal customer satisfaction.

• Built and deployed production AI infrastructure that scaled to support enterprise‑grade reliability and observability.
• Delivered self‑service tools or APIs that enabled multiple product teams to accelerate their AI/ML development cycles.
• Implemented evaluation frameworks, A/B testing infrastructure, or monitoring solutions that measured and improved model performance, latency, cost, and quality in production.
• Led initiatives to reduce AI compute costs through optimisation strategies such as intelligent routing or caching.
• Successfully migrated teams from bespoke AI solutions to shared platform services, driving measurable adoption.
• Prioritised and improved…