Applied AI Engineer

Are you looking to make an impactful difference in your work, yourself, and your community? Why settle for just a job when you can land a career? At ICW Group, we are hiring team members who are ready to use their skills, curiosity, and drive to be part of our journey as we strive to transform the insurance carrier space. We're proud to be in business for over 50 years, and its change agents like yourself that will help us continue to deliver our mission to create the best insurance experience possible.

Headquartered in San Diego with regional offices located throughout the United States, ICW Group has been named for ten consecutive years as a Top 50 performing P&C organization offering the stability of a large, profitable and growing company combined with a focus on all things people. It's our team members who make us an employer of choice and the vibrant company we are today.

We strive to make both our internal and external communities better everyday! Learn more about why you want to be here!

The Applied AI Engineer is responsible for developing, deploying, and maintaining applied generative AI solutions that support the organization’s insurance products, internal workflows, and customer experiences. This role focuses on building reliable AI models and pipelines, collaborating closely with cloud engineering, AI, and ML Ops teams, and implementing best practices for model performance, security, and cost efficiency. The engineer will contribute to AI initiatives while developing expertise in generative AI, cloud deployment, and feature engineering using Snowflake.

• Implement end-to-end generative AI solutions, including model fine-tuning, deployment, and testing in production environments.
• Collaborate with cloud engineering, AI, and ML Ops teams to operationalize AI workloads on AWS using services such as Sage Maker, Lambda, ECS/EKS, and S3.
• Build and maintain AI/ML pipelines leveraging Snowflake for feature engineering and data preprocessing.
• Follow organizational security and compliance standards when developing and deploying AI solutions.
• Monitor model performance, troubleshoot issues, and implement optimizations for reliability and cost efficiency.
• Participate in research and evaluation of generative AI technologies to inform project decisions.
• Document AI models, pipelines, and processes for team knowledge sharing and compliance.
• Support senior engineers in AI architecture reviews, code reviews, and operationalization planning.

• Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or a related technical discipline.
• 3+ years of experience in AI/ML engineering, with at least 1–2 years focused on generative AI or large language models.
• Hands‑on experience deploying AI/ML models in cloud environments, preferably AWS.
• Familiarity with Snowflake for AI/ML feature engineering and data integration.
• Experience in regulated industries or highly data‑sensitive environments is a plus.

• AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect is preferred but not required.
• Optional AI/ML certifications (e.g., Tensor Flow Developer, Hugging Face Course, or Generative AI specialization) are a plus.
• Awareness of compliance and governance standards (SOC 2, ISO 27001) is beneficial.

• Understanding of generative AI architectures, including transformers and retrieval‑augmented generation.
• Proficiency in Python and ML frameworks such as PyTorch, Tensor Flow, or Hugging Face Transformers.
• Knowledge of MLOps practices, including CI/CD pipelines, testing, and monitoring.
• Familiarity with containerization (Docker, Kubernetes) and deployment in cloud environments.
• Solid understanding of data pipelines, Snowflake architecture, and ETL/ELT best practices.
• Awareness of security, governance, and compliance considerations for AI/ML systems.
• Ability to troubleshoot model performance and optimize workloads for efficiency and cost.
• Strong problem‑solving and collaboration skills, with effective communication to both technical and non‑technical stakeholders.