AI Engineer

We’re using AI to reshape how insurance works and ultimately, how consumers are protected. We work with Fortune 100 insurers to automate their regulatory busywork so they can focus on what matters: insuring people. We’re a team backed by strategic investors, building something meaningful, working hard, and (most days) having fun along the way.

The Role 

We are looking for a hands-on, product-oriented AI Engineer who wants to solve deep architectural and  machine learning challenges. You will take ownership of our core AI intelligence layer - orchestrating multi-agent systems, advanced RAG frameworks, and domain-specific LLM reasoning chains designed for complex, high-stakes legal and  regulatory documentation where accuracy, auditability, and performance are critical. 

What You’ll Own 

• AI Architecture & Agent Orchestration: Own, design, and scale complex AI workflows leveraging  Multi-Agent architectures, RAG, Knowledge Graphs, and structured data extraction. 
• Model Benchmarking & Evaluation: Build robust evaluation pipelines, benchmarking tools, and  guardrails to measure and optimize model accuracy, legal reasoning, and factual alignment on  domain-specific datasets. 
• Production AI Infrastructure: Act as the technical bridge connecting cutting-edge AI/LLM models  with our distributed backend architecture (Python/TypeScript), ensuring low latency, high  availability, and enterprise auditability.
• Product-Driven Execution: Partner closely with Product, CTO, and R&D leads to rapidly move features from experimental AI capabilities into clean, production-ready systems. 

What We’re Looking For 

• Core Experience: 3+ years of software engineering experience, with at least 1-2+ years dedicated to  hands-on AI/LLM engineering in production environments. 
• Deep AI Stack Knowledge: Strong practical experience with modern AI technologies - LLMs (API based and open-source), Multi-Agent frameworks, advanced RAG architectures, Vector Databases,  Knowledge Graphs, and prompt engineering/fine-tuning. 
• Engineering Fundamentals: Strong familiarity with core backend concepts required for production systems - specifically Concurrency and Parallelism, Networking, Caching (prompt & semantic caching), and API design. Solid understanding of backend architecture, microservices, and data pipelines. 
• Production & Quality Mindset: Track record of taking AI models beyond initial prompts/prototypes into reliable, auditable, enterprise-ready features. 
• Startup DNA: Comfortable with ambiguity, proactive, high-agency, and thrives in a high-velocity environment without needing detailed specifications to start building.

Why Blaise

You’ll join at a pivotal Series A moment, working directly with Fortune 100 customers in a $2 Trillion industry. You won't be implementing someone else's playbook; you'll be writing it.

We offer clear long-term career growth paths into Product Operations, Marketing Ops / Demand Gen, or RevOps Leadership. If you want to build the operating system of a fast-scaling startup from the ground up, this is that opportunity.