Senior Machine learning Engineer

guidewire; United States; Remote; USD 162,000 - 258,000; Full time

Job summary

Guidewire is seeking a technical leader to drive GenAI and LLM capabilities across data prep, experimentation, deployment, and monitoring for insurance workflows. You will mentor engineers and data scientists while shaping secure, scalable AI services that deliver concrete customer outcomes.

Collaborating with product, engineering, and business teams, you will align capabilities with roadmaps, governance, and cost optimization, while advancing reliability and performance of AI solutions.

Qualifications

• Five or more years of machine learning or data science experience, including production ML system delivery.
• Strong Python skills and experience building scalable ML or LLM services and pipelines.
• Knowledge of model lifecycle practices such as CI/CD, experiment tracking, model registries, monitoring, and drift detection.
• Experience with classical and gradient-boosting models, neural networks, transformers, and LLM fine-tuning or inference.
• Hands-on familiarity with prompt design, RAG, vector databases, and evaluation of LLM quality, safety, and robustness.
• Ability to lead technical work, collaborate across functions, and communicate design decisions effectively.

Responsibilities

• Design and evolve end-to-end GenAI solutions, including ingestion, feature pipelines, training, fine-tuning, deployment, and monitoring.
• Build robust machine learning and LLM pipelines for applications such as claim summaries, underwriting support, pricing insights, and developer tools.
• Apply prompt engineering, retrieval-augmented generation, vector search, and fine-tuning to improve reliability and usefulness.
• Establish MLOps practices for delivery, experimentation, evaluation, observability, and responsible AI.
• Work with product, security, finance, and go-to-market partners to align capabilities with roadmap, governance, and customer needs.
• Review designs and code, mentor engineers and data scientists, and improve performance, reliability, and cost efficiency.

Skills

Production ML systems

Python

CI/CD & ML lifecycle

LLMs/Transformers

Prompt design

Vector databases

Cross-functional leadership

Tools

AWS (SageMaker, S3, EC2, RDS)

Experiment tracking tools

Model registries

Vector search tools

Job description

Role overview

Provide technical leadership for generative AI and large language model capabilities, from data preparation and experimentation through deployment and production monitoring. The work focuses on building secure, scalable AI services for insurance workflows and collaborating across product, engineering, and business teams to deliver practical customer outcomes.

Responsibilities

• Design and evolve end-to-end GenAI solutions, including ingestion, feature pipelines, training, fine-tuning, deployment, and monitoring.
• Build robust machine learning and LLM pipelines for applications such as claim summaries, underwriting support, pricing insights, and developer tools.
• Apply prompt engineering, retrieval-augmented generation, vector search, and fine-tuning to improve reliability and usefulness.
• Establish MLOps practices for delivery, experimentation, evaluation, observability, and responsible AI.
• Work with product, security, finance, and go-to-market partners to align capabilities with roadmap, governance, and customer needs.
• Review designs and code, mentor engineers and data scientists, and improve performance, reliability, and cost efficiency.

Requirements

• Five or more years of machine learning or data science experience, including production ML system delivery.
• Strong Python skills and experience building scalable ML or LLM services and pipelines.
• Knowledge of model lifecycle practices such as CI/CD, experiment tracking, model registries, monitoring, and drift detection.
• Experience with classical and gradient-boosting models, neural networks, transformers, and LLM fine-tuning or inference.
• Hands-on familiarity with prompt design, RAG, vector databases, and evaluation of LLM quality, safety, and robustness.
• Ability to lead technical work, collaborate across functions, and communicate design decisions effectively.

Nice to have

• Experience delivering ML workloads on AWS, including services such as S3, EC2, RDS, or SageMaker.
• Background applying AI to insurance or other business workflows.

Benefits and work setup

• The listing gives a US base salary range of $162,000–$258,000, dependent on factors including experience, skills, and location.
• It describes health, dental, and vision coverage, paid time off, and a retirement plan for eligible roles; some positions may also qualify for bonus or incentive awards.