Generative AI Engineer

Phaxis; Nashville (TN); On-site; USD 120,000 - 160,000; Full time

Job summary

Phaxis in Nashville is seeking an AI/ML engineer to design and develop AI-powered solutions using Python, machine learning, and large language model technologies to address business and operational needs.

You will build and integrate LLM-based applications, develop data pipelines and transformation workflows with Python, SQL, dbt, and cloud services, and collaborate with stakeholders to translate requirements into scalable AI and analytics capabilities.

Responsibilities

• Design and develop AI-powered solutions using Python, machine learning, and large language model technologies to address business and operational needs.
• Build and integrate LLM-based applications, including context engineering, prompt workflows, and AI-assisted analytics capabilities.
• Develop data pipelines and transformation workflows using Python, SQL, dbt, and cloud-based technologies to prepare high-quality data for AI and analytics applications.
• Develop predictive models using machine learning techniques such as Random Forest, clustering, time-series forecasting, NLP, and recommendation algorithms.
• Integrate AI solutions with analytics platforms and business intelligence tools to enable self-service insights and automated decision-making.
• Process and analyze large-scale datasets using AWS services, Spark, and Python to identify trends, develop features, and support data-driven solutions.
• Partner with business, clinical, operations, and technical stakeholders to translate complex requirements into scalable AI and analytics solutions.
• Evaluate and optimize AI models and data workflows for accuracy, performance, scalability, and practical business impact.

Job description

• Design and develop AI-powered solutions using Python, machine learning, and large language model technologies to address business and operational needs.
• Build and integrate LLM-based applications, including context engineering, prompt workflows, and AI-assisted analytics capabilities.
• Develop data pipelines and transformation workflows using Python, SQL, dbt, and cloud-based technologies to prepare high-quality data for AI and analytics applications.
• Develop predictive models using machine learning techniques such as Random Forest, clustering, time-series forecasting, NLP, and recommendation algorithms.
• Integrate AI solutions with analytics platforms and business intelligence tools to enable self-service insights and automated decision-making.
• Process and analyze large-scale datasets using AWS services, Spark, and Python to identify trends, develop features, and support data-driven solutions.
• Partner with business, clinical, operations, and technical stakeholders to translate complex requirements into scalable AI and analytics solutions.
• Evaluate and optimize AI models and data workflows for accuracy, performance, scalability, and practical business impact.