Staff AI Engineer

Newfront

2d•$200,000 - $300,000•Hybrid

About The Position

Newfront (a WTW company) embraces a flexible work environment and supports employees working remotely, hybrid, or in-office. You'll join a team creating agentic services that invisibly handle the routine insurance work that consumes 80% of brokers' time, freeing them to focus on high-stakes strategy and relationship building. We're deploying autonomous systems that act as placement agents, fractional AI analysts, and intelligent workflow orchestrators – not just augmenting human work, but fully automating entire categories of tasks. Our approach is radically different: small, specialized teams that embed directly with the business, building production systems that deliver immediate value. This position is a salary, exempt, and full-time position and reports to the VP of Engineering, Data. This is a US-based remote or hybrid role with the option to work from any of Newfront's office locations. #LI-Remote

Requirements

BS, MS or PhD in computer science, or related field, or equivalent work experience is required.
8+ years of experience in software engineering, machine learning, or AI/ML application development is required.
Strong programming skills in Python.
Experience with modern development and deployment practices (e.g., containerization, CI/CD).
A track record of leading AI/ML projects.
Passion for staying up-to-date with the latest advancements in AI/ML and applying them to real-world problems.
Strong problem-solving skills and the ability to take a pragmatic and efficient approach to tackling challenges.
Excellent collaboration and communication skills, with the ability to effectively communicate complex technical concepts to non-technical stakeholders.

Nice To Haves

Experience deploying or leveraging machine learning models and Large Language Models (LLMs) to power business applications.
Knowledge of state-of-the-art LLM techniques, models, and vendors.
Familiarity with LLM and related frameworks, including extracting structured data from unstructured text.
Experience with popular AI/ML libraries and frameworks.
Familiarity with DevOps practices, cloud infrastructure, authorization, authentication, and search infrastructure.
Understanding of machine learning essentials and the ability to collaborate effectively with data scientists.
Experience with JavaScript backend frameworks such as Node.js, as well as knowledge of TypeScript.

Responsibilities

Build 0-1 AI systems that transform core brokering workflows – from intelligent submission processing that auto-extracts data from 100+ page documents and routes complex commercial policies between AI agents and expert brokers, to autonomous AI that enables brokers to manage 1000+ client renewals simultaneously, owning the full stack from prototype to production.
Embed directly with brokering teams, underwriters, and operations to identify automation opportunities, building solutions side-by-side with the people who use them daily. You'll be acting as an internal FDE, where you're both engineer and business strategist.
Ship production-ready AI features that deliver measurable impact – reducing quote turnaround time by 80%, increasing broker capacity by 10x, and improving placement success rates through intelligent market matching and risk assessment.
Establish rigorous evaluation frameworks for AI in regulated insurance contexts, implementing comprehensive testing pipelines, hallucination detection, and accuracy monitoring to ensure our AI systems meet both compliance requirements and performance targets.
Drive rapid experimentation cycles using modern AI development practices, moving from concept to deployed solution in weeks while maintaining quality through automated testing and progressive rollout strategies.
Develop the next generation of AI talent by mentoring engineers on production AI systems, sharing learnings from real-world deployments, and establishing best practices for building reliable AI in financial services.
Pioneer the application of cutting-edge AI research to insurance challenges, from multi-agent systems for complex underwriting to advanced RAG architectures for policy knowledge management.
Own the complete product development lifecycle for AI initiatives, from initial problem discovery and technical design through frontend interfaces that make AI capabilities intuitive for non-technical users.