Lead Field AI Engineer — Agentic AI & Production Deployments

PepsiCo; Plano (TX); On-site; USD 124,000 - 207,000; Full time

Benefits offered by this job

Medical, Dental, Vision

Paid time off

Bonus 15%

Retirement plan

Job summary

PepsiCo is seeking a Forward Deployed AI Engineering Lead to design and deliver production-grade agent capabilities across cloud and edge environments. You will own end-to-end delivery of agentic modules, optimize performance, and drive adoption by embedding within transformation teams and BU stakeholders.

You will translate complex business workflows into measurable agent outcomes, identify risks, and ensure scalable solutions across domains with a strong emphasis on collaboration with platform

Qualifications

• Bachelor’s degree in CS/AI/ML required or equivalent experience.
• Master’s degree preferred.
• 12+ years of software lifecycle experience.
• Expertise in ML development and engineering.
• Proven experience shipping LLM/agent solutions to production with measurable quality and operational practices.

Responsibilities

• Architect and deploy agent solutions in client environments with customized integrations.
• Develop evaluation frameworks and monitoring for production deployments.
• Lead on-site performance optimization within client constraints.
• Create playbooks and knowledge transfer materials for future deployments.
• Mentor client teams to build internal AI implementation capabilities.

Skills

Python

Java

Distributed systems

LLM/Agent systems

Prompt engineering

Docker

Kubernetes

On-site deployment

Education

Bachelor’s in CS/AI/ML

Master's preferred

Tools

FastAPI

LangChain

LangGraph

CrewAI

MLflow

Job description

PepsiCo is seeking a Forward Deployed AI Engineering Lead to design and deliver production-grade agent capabilities across cloud and edge environments. You will own end-to-end delivery of agentic modules, optimize performance, and drive adoption by embedding within transformation teams and BU stakeholders.

You will translate complex business workflows into measurable agent outcomes, identify risks, and ensure scalable solutions across domains with a strong emphasis on collaboration with platform