Applied AI Engineer

ICW INSURANCE COMPANY OF THE WEST; Baxter Village (SC); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

Competitive benefits package, including medical, dental, and vision plans

401(k) retirement plan with company match

Bonus potential

Paid Time Off and paid holidays

Job summary

ICW INSURANCE COMPANY OF THE WEST is seeking an Applied AI Engineer responsible for developing and maintaining AI solutions that enhance customer experience and internal workflows. The position involves implementing generative AI solutions and working closely with cloud engineering teams on AWS.

Ideal candidates should have a Bachelor’s degree in Computer Science or related fields, alongside extensive knowledge of generative AI and at least 3 years of AI/ML engineering experience. The role offers a competitive benefits package including health insurance and a 401(k) plan.

Qualifications

• 3+ years of experience in AI/ML engineering, with focus on generative AI.
• Hands-on experience deploying AI/ML models in cloud environments, preferably AWS.
• Familiarity with Snowflake for data integration and feature engineering.

Responsibilities

• Implement end-to-end generative AI solutions.
• Collaborate with teams to operationalize AI workloads on AWS.
• Monitor model performance and troubleshoot issues.

Skills

Generative AI architectures

Python

MLOps practices

Snowflake

Problem-solving skills

Collaboration skills

Education

Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or related discipline

Tools

AWS (SageMaker, Lambda, ECS/EKS, S3)

Docker

Kubernetes

PyTorch

TensorFlow

Job description

Purpose of the Job

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.

Essential Duties and Responsibilities

• 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 SageMaker, 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.

Education and Experience

• 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.

Certificates, Licenses, and Registrations

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

Knowledge and Skills

• Understanding of generative AI architectures, including transformers and retrieval‑augmented generation.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, 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.
• Aware 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.

Supervisory Responsibilities

This position has no supervisory responsibility but may mentor and train junior engineers.

Physical Requirements

Office environment – employees are regularly required to sit, walk, stand, talk, and hear. Employees may need to reach with hands and arms; stoop, kneel, crouch, or crawl, and occasionally lift or move up to 30 pounds. Employees must have visual acuity and be capable of operating and viewing computers and other electronic devices for extended periods of time.

Work Environment

This position operates in an office environment and requires the frequent use of a computer, telephone, copier, and other standard office equipment. No sponsorship for employment is being offered.

Benefits

• Competitive benefits package, including medical, dental, and vision plans.
• 401(k) retirement plan with company match.
• Bonus potential for all positions.
• Paid Time Off and paid holidays throughout the calendar year.

Equal Opportunity Statement

ICW Group is committed to creating a diverse environment and is proud to be an Equal Opportunity Employer. ICW Group will not discriminate against an applicant or employee on the basis of race, color, religion, national origin, ancestry, sex/gender, age, physical or mental disability, military or veteran status, genetic information, sexual orientation, gender identity, gender expression, marital status, or any other characteristic protected by applicable federal, state or local law.