Business Services & Consulting • all cities, OH 36
Engineer, Artificial Intelligence (36)
all cities, OH 36On-sitePosted 1 day ago
Business Services & Consulting
About the Role
Artificial Intelligence Engineer
The Artificial Intelligence Engineer supports the development, deployment, and optimization of AI-driven solutions that enhance Prosperity's retail, product, life insurance, and annuity business lines. This role contributes to building models, intelligent automation, and data-driven tools that streamline operations, improve advisor and customer experiences, and support the company's strategic digital transformation.
The ideal candidate brings hands-on experience with machine learning, natural language processing, and applied AI tools, along with an understanding of the life and annuity industry. This person is curious, analytical, and eager to grow while working cross-functionally with Product, Retail, Operations, Finance and Technology partners.
Major Duties and Responsibilities
Applied Machine Learning: Experience building and deploying ML models, including regression, classification, recommendation engines, or predictive analytics.
NLP & Generative AI Foundations: Understanding of text analytics, large language models, and conversational AI tools.
Programming & Automation: Proficiency with Python, SQL, and common data science libraries (e.g., Pandas, Scikit-Learn, TensorFlow, PyTorch).
Life & Annuity Domain Knowledge: Familiarity with product structures, customer journeys, and distribution processes in life insurance and annuities.
Data Analysis & Feature Engineering: Ability to clean, prepare, and analyze structured and unstructured data.
Model Evaluation & Monitoring: Understanding of model accuracy, drift, explainability, and continuous improvement approaches.
Business Collaboration: Ability to translate business needs into technical solutions and communicate complex concepts clearly.
Problem-Solving: Strong analytical mindset and creativity in identifying automation and AI opportunities.
Adaptability: Comfort learning new tools, working in an evolving AI landscape, and supporting emerging business demands.
Tool Proficiency: Experience with cloud platforms (AWS, Azure, GCP), version control (Git), and MLOps or workflow-automation tools is a plus.
Job Qualifications
Bachelor's degree in Computer or Data Science, Engineering, Mathematics, or related field.
1–5 years of experience in AI, machine learning, data science, or applied analytics.
Experience working with Python, SQL, and common ML frameworks.
Understanding of life insurance and annuity products—or strong willingness to learn quickly.
Exposure to cloud environments (AWS, Azure, or GCP).
Strong communication and collaboration skills supporting cross-functional teams.
Ability to translate complex data concepts into actionable business recommendations.
Curiosity, initiative, and a growth mindset—eager to explore emerging AI technologies.
Core Competencies
Analytical thinking and problem-solving
Technical curiosity and continuous learning
Collaboration and communication
Accountability and follow-through
Innovation and applied creativity
Adaptability in a fast-changing, complex, digital environment
Artificial Intelligence Engineer
The Artificial Intelligence Engineer supports the development, deployment, and optimization of AI-driven solutions that enhance Prosperity's retail, product, life insurance, and annuity business lines. This role contributes to building models, intelligent automation, and data-driven tools that streamline operations, improve advisor and customer experiences, and support the company's strategic digital transformation.
The ideal candidate brings hands-on experience with machine learning, natural language processing, and applied AI tools, along with an understanding of the life and annuity industry. This person is curious, analytical, and eager to grow while working cross-functionally with Product, Retail, Operations, Finance and Technology partners.
Major Duties and Responsibilities
Applied Machine Learning: Experience building and deploying ML models, including regression, classification, recommendation engines, or predictive analytics.
NLP & Generative AI Foundations: Understanding of text analytics, large language models, and conversational AI tools.
Programming & Automation: Proficiency with Python, SQL, and common data science libraries (e.g., Pandas, Scikit-Learn, TensorFlow, PyTorch).
Life & Annuity Domain Knowledge: Familiarity with product structures, customer journeys, and distribution processes in life insurance and annuities.
Data Analysis & Feature Engineering: Ability to clean, prepare, and analyze structured and unstructured data.
Model Evaluation & Monitoring: Understanding of model accuracy, drift, explainability, and continuous improvement approaches.
Business Collaboration: Ability to translate business needs into technical solutions and communicate complex concepts clearly.
Problem-Solving: Strong analytical mindset and creativity in identifying automation and AI opportunities.
Adaptability: Comfort learning new tools, working in an evolving AI landscape, and supporting emerging business demands.
Tool Proficiency: Experience with cloud platforms (AWS, Azure, GCP), version control (Git), and MLOps or workflow-automation tools is a plus.
Job Qualifications
Bachelor's degree in Computer or Data Science, Engineering, Mathematics, or related field.
1–5 years of experience in AI, machine learning, data science, or applied analytics.
Experience working with Python, SQL, and common ML frameworks.
Understanding of life insurance and annuity products—or strong willingness to learn quickly.
Exposure to cloud environments (AWS, Azure, or GCP).
Strong communication and collaboration skills supporting cross-functional teams.
Ability to translate complex data concepts into actionable business recommendations.
Curiosity, initiative, and a growth mindset—eager to explore emerging AI technologies.
Core Competencies
Analytical thinking and problem-solving
Technical curiosity and continuous learning
Collaboration and communication
Accountability and follow-through
Innovation and applied creativity
Adaptability in a fast-changing, complex, digital environment
What You'll Do
Applied Machine Learning: Experience building and deploying ML models, including regression, classification, recommendation engines, or predictive analytics.
NLP & Generative AI Foundations: Understanding of text analytics, large language models, and conversational AI tools.
Programming & Automation: Proficiency with Python, SQL, and common data science libraries (e.g., Pandas, Scikit-Learn, TensorFlow, PyTorch).
Life & Annuity Domain Knowledge: Familiarity with product structures, customer journeys, and distribution processes in life insurance and annuities.
Data Analysis & Feature Engineering: Ability to clean, prepare, and analyze structured and unstructured data.
Model Evaluation & Monitoring: Understanding of model accuracy, drift, explainability, and continuous improvement approaches.
