Application SRE AI Solutions Engineer

We are looking for a self-driven, software engineering mindset AI SRE engineer to

• Defining and implementing robust architectural patterns for Machine Learning (ML) models, Large Language Models (LLMs), AI agents, computer vision solutions to help drive new shift left activities critical to apply Site Reliability Engineering (SRE) and quality assurance principles within the application design & architecture / Project roadmap that enables resilient outcomes.
• Collaborate closely with PepsiCos Data and AI Architecture team to ensure these patterns align with our Data and AI Strategy and technical standards.
• Apply pre-emptive approach into production minimizing business impact, via SRE-driven orchestration of connecting all components of the ecosystem diagnosing anomalies prior to user & remediating through automation & AI.

This is a critical enabler achieving a high resiliency during operations and also continuously improving through design during the software development lifecycle.

The DPA SRE Principal Engineer is integral part of the global team with its main purpose to provide a delightful customer experience for the user of the global consumer, commercial, supply chain and enablement functions in the PepsiCo digital products application portfolio of 260+ applications, enabling a full SRE Practice incident prevention / proactive resolution model.

The scope of this role is focused on the cloud architecture application full stack development, B2B pepsi connect and Direct to Customer and other S&T roadmap applications.

Ensures that PepsiCo DPA applications service performance, reliability and availability expected by our customers and internal groups.

It requires a blend of technical expertise on SRE tools, modern applications cloud architecture i.e. full stack, IT operations experience, and analytics & influence skills.

• Architectural Pattern Definition: Define platform and model development patterns for ML models, LLMs, and AI agents in collaboration with the Data and AI Architecture team.
• AI Observability: Establish and standardize AI observability patterns for ML, computer vision systems, LLMs, and AI agents to enable robust model monitoring, explainability, and drift detection.
• Cross-Functional Collaboration: Engage with cross-functional teams (data science, engineering, operations, etc.) to standardize AI architecture practices and patterns across the enterprise.