The AI Engineering Analyst will internalize and scale agentic statistical programming platform (Helix). The initial focus of the role will be to contribute towards solution testing, gain a strong understanding of the system design and configuration. Next phase will be focused on developing agents to scale the platform across multiple therapeutic areas and study designs. In addition, the role will help optimize business workflows. Long-term, the role will focus on building and deploying AI solutions including agent platforms, AI co-pilots, code generation harnesses, evaluation frameworks, and agentic research pipelines using modern AI and AbbVie enterprise frameworks integrated with internal DSS systems and data platforms.

The hired associated will partner closely with internal stakeholders such as statisticians, stats programmers, data scientists, and other business teams to understand user needs, and rapidly iterate on building AI solutions, and evolve successful use cases into reusable capabilities. Test and evaluate foundation models, agent architectures, and AI workflows, making practical technical decisions based on reliability, security, cost, business impact, etc. Deliver high-quality, maintainable AI products and solutions through sound AI engineering practices, automated testing, and thoughtful code deployments.

Responsibilities

• Build production-grade AI solutions including agent platforms, AI co-pilots, code generation harnesses, evaluation frameworks, and agentic research pipelines using modern AI and AbbVie enterprise frameworks integrated with internal systems and data platforms
• Collaborates with Statisticians & Data Scientists to enable streamlined data flow for the Data & Statistical Sciences capabilities across clinical development
• Evaluate foundation models, agent architectures, and AI workflows, making practical technical decisions based on reliability, security, cost, business impact, etc.
• Operate, monitor, and continuously improve production AI solutions by optimizing quality, reliability, latency, cost efficiency, governance, and user outcomes.
• Develop, construct, test and maintain architectures (such as databases and large-scale process systems) to support AI & Analytics projects within Clinical Development
• Build data products and service processes which perform data transformation, metadata extraction, workload management and error processing management
• Implement standardized, automated operational and quality control processes to deliver accurate and timely data and reporting
• Adhere to best practices for coding, testing and designing reusable code/component
• Contribute to the discovery and understanding of new tools, and techniques and propose improvements to the data pipeline
• Develop data set processes for data modeling, mining and production