Automation Engineer, Financial Systems AI

Key Responsibilities

• Designs, builds, and deploys AI-powered tools and automated workflows to enhance Finance organization efficiency and decision-making.
• Applies modern data engineering and AI development practices to deliver scalable, governed solutions.
• Collaborates with Finance stakeholders and cross-functional teams to translate business problems into working software, ensuring compliance with Centene data security, compliance, and regulatory requirements.
• Develops and supports end-to-end AI and automated solutions for Finance operations, including data pipelines, reporting applications, workflow automation, and AI-assisted analysis tools.
• Identifies manual or inefficient processes and designs technology-enabled solutions to reduce effort and improve data quality.
• Assists in establishing and maintaining development standards, including version control practices, code review workflows, and continuous integration and delivery (CI/CD) pipelines.
• Contributes to the team's AI governance framework by registering tools, participating in design and review processes, and ensuring adherence to Centene data classification and compliance requirements.
• Builds and maintains data transformation and integration workflows for Finance AI applications, ensuring data quality, consistency, and reliability.
• Develops interactive Finance applications to surface AI-generated insights, automate reporting tasks, and provide self-service capabilities.
• Provides ongoing support, troubleshooting, and incremental enhancements for deployed solutions.
• Complies with all policies and standards.

Requirements & Tech Stack

• Software development principles.
• Data engineering concepts.
• Familiarity with automation tools, scripting languages, or analytics platforms.
• Basic understanding of Artificial Intelligence (AI) or machine learning (ML) concepts, with an emphasis on applied use.
• Ability to translate defined business requirements into technical solutions with guidance.
• Strong attention to detail and commitment to data accuracy and quality.
• Effective written and verbal communication skills.
• Ability to collaborate across technical and non-technical teams.

Qualifications

• A Bachelor's degree in Computer Science, Data Engineering, or Information Systems.
• 0 – 2 years of related experience in software development, data engineering, or AI/Machine Learning systems.