AI/ML Engineer II - Production ML & MLOps

Socket.dev; Des Moines (IA); On-site; USD 100,000 - 140,000; Full time

Job summary

American Equity Investment Life Insurance Company seeks an AI/ML Engineer II to develop, deploy, and support production ML/AI solutions. You will collaborate with data scientists, software/data engineers, and business stakeholders to translate prototypes into reliable, scalable applications.

You will build pipelines, implement MLOps, and develop modular Python services and APIs while applying governance and security standards throughout the lifecycle.

Qualifications

• Bachelor’s degree in computer science, software engineering, data science, statistics, mathematics, information technology, or a related field.
• Plus four (4) or more years of progressively responsible related experience in machine learning engineering, software engineering, data engineering, or a related discipline.
• A postgraduate degree may substitute for a portion of the experience requirement.

Responsibilities

• Translate ML prototypes and analytical solutions into scalable production software.
• Develop, deploy, monitor, and support ML models and data pipelines.
• Design modular Python applications, services, and APIs for ML/AI use cases.
• Apply MLOps practices: source control, CI/CD, testing, documentation, model versioning, monitoring.
• Develop AI-enabled applications including retrieval-augmented generation and enterprise search.

Skills

Python

SQL

MLOps

APIs

CI/CD

Testing

Security & Governance

Education

Bachelor’s degree in CS/Software Eng/Data Science

4+ years related experience

Tools

AWS SageMaker

Azure Machine Learning

Snowflake

Redshift

BigQuery

Snowflake Cortex

AWS Bedrock

Salesforce Agentforce

Microsoft Copilot Studio

Anthropic Claude

Job description

American Equity Investment Life Insurance Company seeks an AI/ML Engineer II to develop, deploy, and support production ML/AI solutions. You will collaborate with data scientists, software/data engineers, and business stakeholders to translate prototypes into reliable, scalable applications.

You will build pipelines, implement MLOps, and develop modular Python services and APIs while applying governance and security standards throughout the lifecycle.