Gen AI Engineer

Infosys seeks a Gen AI Engineer to join its onsite team in Charlotte, NC. The role centers on data extraction, transformation and preparation, and on developing, testing, validating, and deploying ML and DL models, including large language models. You will collaborate with business and technology teams to operationalize analytics solutions and translate requirements into actionable models.

Responsibilities

• Participate in data extraction, transformation and preparation.
• Resolve common data issues and ensure quality for model development.
• Develop models using statistical or machine learning techniques and collaborate with technology teams to operationalize them into analytics tools or scripts.
• Participate in model testing and validation, selecting the best-performing algorithms based on statistical and business metrics.
• Contribute to the development of advanced analytics and machine learning or deep learning models including LLMs using predefined processes and tools like SAS and R/ Python.
• Define analytics problems and perform visualization, analysis, and predictive modeling with senior support.
• Identify data sources and extract from RDBMS and develop UI/UX for client usage.
• Engage in model performance, make minor adjustments, escalate risks or compliance concerns and generate reports on deviations or schedule slippages.
• Document model development, testing, and deployment activities for reproducibility.
• Work closely with business and technology teams to translate requirements into actionable models, while effectively communicating results.
• Apply predefined quality measurement frameworks, if any, to individual project tasks.
• Deploy analytics tools in test and production environments, ensuring they meet operational requirements.

Technologies

• SAS
• R
• Python

Your Contribution to the Team

• Strong analytical and problem-solving mindset with hands-on model development skills.
• Ability to translate business needs into actionable analytics solutions.
• Focus on data quality, validation and performance optimization.
• Effective collaboration with business and technology stakeholders.
• Commitment to continuous learning, knowledge sharing, fostering team development.