Artificial Intelligence/Machine Learning Engineer II

American Equity•Des Moines, IA

1d•Onsite

About The Position

The Artificial Intelligence/Machine Learning Engineer II develops, deploys, and supports production machine learning and AI-enabled solutions. This role partners with data scientists, software engineers, data engineers, architects, and business stakeholders to translate analytical prototypes and business requirements into reliable, scalable, and maintainable applications. Responsibilities include developing production machine learning pipelines, implementing MLOps practices, building software and APIs, and supporting emerging AI use cases such as retrieval-augmented generation and agent-based solutions. The Machine Learning Engineer II applies established engineering, security, governance, and responsible AI standards throughout the development lifecycle.

Requirements

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; or an equivalent combination of education and experience.
Experience developing and deploying production machine learning or data-driven applications.
Demonstrated experience developing software in Python.
Experience with cloud-based machine learning platforms such as AWS SageMaker, Azure Machine Learning, or similar platforms.
Experience with cloud data platforms such as Snowflake, Redshift, BigQuery, or similar technologies.
Experience with SQL, APIs, automated testing, version control, and CI/CD.
Experience supporting production applications, pipelines, or machine learning solutions.
Proficient Python software development skills, including modular design, testing, debugging, and dependency management.
Strong SQL skills and working knowledge of machine learning concepts, common Python-based ML libraries, and MLOps practices.
Understanding of application architecture, cloud development, authentication, logging, monitoring, and error handling.
Familiarity with generative AI concepts, including large language models, prompting, embeddings, retrieval-augmented generation, agents, and grounding.
Familiarity with AI development platforms such as AWS Bedrock, Salesforce Agentforce, Microsoft Copilot Studio, Anthropic Claude, Snowflake Cortex, or similar technologies.
Ability to assess and improve the quality of AI application outputs through testing and established evaluation practices.
Understanding of AI security, privacy, responsible AI, and data governance principles.
Ability to learn and apply emerging technologies within defined architectural and governance standards.

Nice To Haves

Experience building generative AI applications, RAG pipelines, enterprise search solutions, or AI agents.
Experience with AWS Bedrock, Salesforce Agentforce, Microsoft Copilot Studio, Anthropic Claude, Snowflake Cortex, or similar platforms.
Experience with containers and cloud deployment technologies.
Experience implementing monitoring, observability, or evaluation for machine learning or AI applications.
A postgraduate degree may substitute for a portion of the experience requirement under appropriate circumstances.

Responsibilities

Partners with data scientists to translate machine learning prototypes and analytical solutions into tested, scalable, and maintainable production software.
Develops, deploys, monitors, and supports machine learning models, data pipelines, and feature pipelines using cloud-based platforms such as AWS SageMaker and Snowflake.
Designs and develops modular, reusable, and maintainable Python applications, services, and APIs that support machine learning and AI use cases.
Applies software engineering and MLOps practices, including source control, code review, automated testing, documentation, CI/CD, model versioning, and production monitoring.
Develops and supports AI-enabled applications, including retrieval-augmented generation, enterprise search, and agent-based workflows.
Configures and develops AI agents and workflows using approved platforms such as Salesforce Agentforce, Microsoft Copilot Studio, AWS Bedrock, Anthropic Claude, Snowflake Cortex, or similar technologies.
Integrates machine learning and AI solutions with approved enterprise data sources, APIs, applications, and business systems.
Tests and evaluates machine learning and AI solutions for quality, reliability, performance, security, grounding, and appropriate handling of unsupported responses.
Applies established architecture, security, privacy, governance, observability, and responsible AI standards.
Collaborates with data science, software engineering, data engineering, architecture, security, DevOps, and business teams to deliver production solutions.
Troubleshoots production issues involving data, pipelines, applications, integrations, machine learning models, and AI-enabled solutions.
Stays current on relevant developments and best practices in software engineering, machine learning, MLOps, and AI engineering.
Performs other related duties as assigned.