AI Engineer Analytics

Infosys is hiring an AI Engineer/Analytics professional in Dallas, TX (onsite) to build predictive and advanced analytics solutions that move from data preparation to scalable deployment. This role includes work on forecasting and LLM-based approaches, along with ongoing model governance and improvement so analytics can stay accurate, reliable, and aligned to business needs.

What you’ll be doing

• Develop data preparation tasks, including identifying patterns or anomalies and ensuring data readiness for advanced modeling.
• Build models for complex use cases, including forecasting models and LLM-based solutions, refining algorithms to match business requirements and enabling smooth, production-ready deployment.
• Perform testing and algorithm optimization to improve performance, reliability, and scalability, and share best practices with team members.
• Design and develop predictive models and data-driven analyses to solve business challenges.
• Build, evaluate, and deploy models, standardize code, and contribute to knowledge management.
• Use SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs and creating innovative, cost-effective solutions.
• Define analytics problems (under guidance as needed), then execute visualization, analysis, and predictive modeling.
• Proactively maintain models, implement improvements for accuracy and reliability, and apply governance controls to mitigate risks and ensure compliance.
• Analyze performance trends, recommend improvements, document discrepancies for escalation, and validate deployment success when deploying complex analytics tools or multi-system integrations.
• Participate in building scripts or templates to support repeated deployment tasks.
• Contribute to analytics solutions, IP asset creation, and training initiatives, including delivering analytics training and creating content.
• Support business planning with data-driven insights, including input for segment and unit-level business plans.
• Apply a predefined quality measurement framework at the individual task level, maintain comprehensive documentation, and participate in knowledge transfer sessions.
• Engage with stakeholders to refine requirements, provide insights, and guide model implementation.
• Contribute to thought leadership such as papers, innovative non-ML, ML, deep learning, and LLM models, and proofs of concept.

Tools and technologies

• SAS
• R
• Python
• LLMs
• Non-ML, ML, and Deep Learning