Senior AI Engineer

About the Role

As a Senior AI Engineer at Newfront, a WTW company, you will play a pivotal role in developing and scaling our AI platform and the AI products built upon it. This position is a full-time, exempt role with the flexibility of being US-remote or hybrid, with options to work from any Newfront office location.

What You’ll Be Responsible For:

• Build and scale the agent runtime at Newfront, encompassing tool use, planning, memory, multi-agent orchestration, human-in-the-loop handoff, and SDKs for product teams to compose agents for brokering, underwriting, and client service workflows.
• Design and own RAG and document understanding pipelines for complex insurance artifacts, including ingestion, chunking, indexing, retrieval, structured extraction, and grounding.
• Build the connector framework for AI agents and pipelines to interact with systems like AMS, carrier portals, email, document stores, and internal services, ensuring first-class auth, rate-limiting, schema discovery, and auditability.
• Establish the evaluation and observability backbone for AI at Newfront, including offline eval harnesses, regression suites, hallucination/grounding checks, online quality, latency, cost telemetry, and dashboards for measuring AI features against compliance and performance targets.
• Own model routing and the model gateway, selecting between hosted frontier models and on-premise/self-hosted models based on use case, balancing quality, latency, cost, and data-residency constraints.
• Stand up and operate on-premise model hosting for regulatory, contractual, or data-sensitivity requirements, involving GPU capacity planning, inference serving, quantization, optimization, isolation, and lifecycle management.
• Ship AI product features end-to-end on top of the platform, from problem discovery with business partners through technical design, implementation, and user-facing surfaces.
• Mentor engineers on production AI systems, review designs, and establish best practices for building reliable AI in financial services.
• Partner with security, privacy, and compliance to embed controls (auth, audit, PII handling, retention, model risk management) directly into the platform.

Qualifications:

• BS, MS or PhD in computer science, or a related field, or equivalent work experience.
• 5+ years of professional software engineering experience with a strong general software development background, focused on building, shipping, and operating production services rather than just notebooks or prototypes.
• Solid fundamentals in API design, data modeling, testing, debugging production systems, code review, and collaborating in a team codebase.
• Strong programming skills in TypeScript, including experience with Node.js or another TypeScript backend framework in production.
• Experience with modern development and deployment practices (e.g., containerization, CI/CD, infrastructure-as-code, production observability).
• A track record of leading AI/ML projects end-to-end, including API design, production operations, and long-term maintenance.
• Experience designing systems for reliability, cost, and scale in production.
• 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 a pragmatic, efficient approach to tackling challenges.
• Excellent collaboration and communication skills, with the ability to partner with product teams and effectively communicate complex technical concepts to non-technical stakeholders.

Preferred Knowledge, Skills, and Abilities:

• Working knowledge of Python and/or Go.
• Experience deploying or leveraging machine learning models and Large Language Models (LLMs) to power business applications at scale.
• Hands-on experience building agent frameworks, tool-use runtimes, RAG systems, connector/integration frameworks, or evaluation harnesses for LLM-based applications.
• Experience self-hosting or fine-tuning open-weight LLMs (e.g., GPU inference serving with vLLM/TGI/TensorRT-LLM, quantization, LoRA/PEFT, on-prem deployment).
• Experience building model gateways or routing layers that span multiple model providers and self-hosted models.
• 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.
• Experience with model risk management, AI governance, or building AI systems in regulated industries.
• Understanding of machine learning essentials and the ability to collaborate effectively with data scientists.