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# GenAI Engineer

- **Company:** The Client
- **Location:** San Antonio, TX, United States
- **Contract:** Permanent contract
- **Skills:** Business Analytics Applications, Data Mining, Relational Databases, Design of User Interfaces, Python (Programming Language), Machine Learning, Performance Tuning, SAS (Software), Large Language Models, Deep Learning
- **Published:** September 3, 2026
- **Apply:** https://www.themuse.com/jobs/infosys/genai-engineer

## About the Role

* 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.

## Description

In the assigned Job Role of Data Science Consultant 1, your Area Of Responsibility will be as below:

 * Participate in data extraction, transformation, and preparation.
 * Resolve common data issues and ensure quality for model development.
 * Participate in developing 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.
 * Participate in the development of advanced analytics and machine learning or deep learning models including LLMs using predefined processes and tools like SAS and R/ Python.
 * Participate in defining analytics problems; execute visualization, analysis, and predictive modeling with senior support.
 * Identify data sources and extract from RDBMS and develop UI/UX for client usage.
 * Participate in model performance, while making minor adjustments, and escalate risks or compliance concerns and generate reports on deviations or schedule slippages.
 * Proactively participate in detailed documentation of model development, testing, and deployment activities for reproducibility.
 * Work closely with business and technology teams to translate requirements into actionable models, while communicating results effectively.
 * Apply predefined quality measurement frameworks, if any, to individual project tasks.
 * Participate in deploying analytics tools in test and production environment, while ensuring they meet operational requirements.


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