remote
Senior AI Engineer
AI Engineer
As a Senior AI Engineer at Newfront, you will design, build, and scale both the core AI platform—including agent runtimes, RAG pipelines, and model hosting—and the AI products built upon it. You will collaborate with product engineering, brokering, and operations leaders to deliver reliable and compliant AI solutions for the financial services industry. This role requires strong software engineering fundamentals and a track record of leading AI/ML projects end-to-end in production environments.
About the role
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.