Gen AI Engineer

Infosys offers a collaborative, on-site Gen AI Engineer role based in Charlotte, NC. This position centers on end-to-end data preparation, model development and deployment of analytics solutions in close partnership with business and technology teams. The role supports continuous learning, knowledge sharing, and team development within a dynamic engineering environment.

Responsibilities

• Contribute to extracting, transforming, and preparing data for modeling.
• Identify and resolve typical data quality issues to support model development.
• Contribute to building models with statistical or machine learning methods and work with technology teams to deploy them as analytics tools or scripts.
• Engage in model testing and validation, selecting algorithms that optimize statistical and business metrics.
• Assist in creating advanced analytics and machine learning or deep learning models, including LLMs, using established processes and tools such as SAS, R, and Python.
• Assist in framing analytics problems and perform visualization, analysis, and predictive modeling with guidance from senior staff.
• Identify data sources from relational databases and develop user interfaces for client usage.
• Contribute to monitoring model performance, apply minor adjustments as needed, escalate risks or compliance concerns, and generate deviation or schedule-slip reports.
• Document model development, testing, and deployment activities thoroughly to ensure reproducibility.
• Collaborate with business and technology teams to translate requirements into actionable models and communicate results effectively.
• Apply any established quality measurement frameworks to project tasks where applicable.
• Assist in deploying analytics tools to test and production environments while ensuring operational readiness.

Technologies

• SAS
• R
• Python

Your contribution to the team

• Analytical problem-solving acumen with practical experience building and tuning models.
• Capacity to convert business requirements into concrete analytics solutions.
• Emphasis on data quality, validation processes, and performance optimization.
• Proven collaboration with both business units and technology teams.
• Dedication to ongoing learning, sharing knowledge, and supporting team growth.