Data Engineer (AI)

TL;DR

Data Engineer (AI): Building and supporting scalable data pipelines, cloud-based platforms, and MLOps services for the Customer Operations Data Science team with an accent on AI and machine learning integration. Focus on developing production-ready data assets, implementing observability, and optimizing omnichannel customer journeys.

Location: Must be based in or able to commute to Columbus, OH, Chicago, IL, Hartford, CT, or Charlotte, NC for a hybrid schedule (3 days/week in office).

Company

hirify.global is a leading insurance company dedicated to providing innovative coverage and AI-driven customer experiences.

What you will do

• Design, build, and maintain scalable ETL/ELT data pipelines and cloud-native integrations.
• Implement data quality controls, monitoring, and observability to ensure reliable data products.
• Support machine learning and AI solutions through data engineering and operationalization.
• Develop CI/CD pipelines and Infrastructure as Code (IaC) for cloud-based deployments.
• Collaborate with Data Science, Architecture, and Business teams to deliver measurable value.
• Troubleshoot and resolve performance issues across data platforms and AI services.

Requirements

• Must be authorized to work in the U.S. without company sponsorship.
• Bachelor's degree in Computer Science, Data Engineering, or related field.
• 2+ years of experience in data engineering or software engineering.
• 2+ years of experience with Python and SQL development.
• Experience with cloud platforms (AWS or GCP) and data warehouse technologies (Snowflake, BigQuery, or Redshift).
• Experience with CI/CD tools and Infrastructure as Code practices.

Nice to have

• Experience with Docker, Kubernetes, and containerized deployments.
• Familiarity with MLOps, dbt, Spark, or Hadoop.
• Exposure to Generative AI, Agentic workflows, and LLM-powered applications.
• Experience working in highly regulated industries like insurance or finance.

Culture & Benefits

• Commitment to building scalable, maintainable, and transparent AI systems.
• Collaborative environment working closely with business and technology partners.
• Focus on practical, evolutionary product delivery and continuous improvement.
• Comprehensive compensation package including base pay, annual bonuses, and long-term incentives.