Senior Applied AI Engineer

ICW Group•San Diego, CA

1d•Hybrid

About The Position

The Senior Applied AI Engineer is responsible for leading the design, development, and operationalization of generative AI solutions that support the insurance company’s business objectives. This role drives the adoption of cutting-edge AI technologies, ensures models are deployed securely, cost-effectively, and in compliance with regulatory and organizational standards, and serves as a mentor and technical authority for the AI and ML Platform team. The engineer will work cross-functionally with cloud engineering, AI, and ML Ops teams to integrate AI capabilities into both customer-facing and internal systems, ensuring reliability, scalability, and measurable business impact.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related technical discipline.
5+ years of experience in AI/ML engineering, with at least 3 years focused on generative AI or large language models in applied settings.
Proven experience deploying and scaling AI/ML models in cloud environments, preferably AWS.
Hands-on experience with Snowflake for AI/ML feature engineering and data integration in a plus.
Prior experience in insurance, financial services, or highly regulated environments is strongly preferred.
Deep understanding of generative AI architectures, including transformers, diffusion models, and retrieval-augmented generation.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.
Strong knowledge of MLOps practices, including CI/CD pipelines, model versioning, testing, and monitoring.
Experience with containerization (Docker, Kubernetes) and orchestration in cloud environments.
Solid understanding of data pipelines, Snowflake architecture, and ETL/ELT best practices.
Strong knowledge of security, governance, and compliance considerations in AI/ML systems.
Experience optimizing AI workloads for cost efficiency without sacrificing performance.
Excellent problem-solving skills, ability to make design tradeoffs in complex systems, and mentor junior team members.
Strong communication skills to translate technical concepts for business stakeholders and cross-functional teams.

Nice To Haves

AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect preferred.
Relevant AI/ML certifications (e.g., TensorFlow Developer, Hugging Face Course, or Generative AI specialization) are a plus.
Compliance or governance-related certifications (e.g., SOC 2, ISO 27001) are a differentiator.

Responsibilities

Design, implement, and maintain end-to-end generative AI solutions, including model fine-tuning, deployment, and monitoring in production environments.
Lead applied AI projects that integrate generative models into insurance products, customer engagement tools, and internal operational workflows.
Collaborate with cloud engineering, AI, and ML Ops engineers to operationalize AI workloads on AWS using services such as SageMaker, Lambda, ECS/EKS, and S3.
Build robust AI/ML pipelines leveraging Snowflake for feature engineering and data preprocessing.
Ensure AI/ML systems comply with organizational security policies, regulatory requirements, and data privacy standards.
Establish best practices for model governance, explainability, monitoring, and risk mitigation in regulated environments.
Monitor and optimize AI workloads for cost efficiency in cloud environments, balancing performance, scalability, and budget considerations.
Mentor mid-level engineers, reviewing code, architecture, and model design for quality and compliance.
Drive research and evaluation of emerging generative AI technologies and frameworks, recommending adoption strategies for the organization.
Troubleshoot performance and scalability issues in production AI systems and optimize models for cost and latency.