Lead AI Engineer

Overview As a Forward Deployed AI Engineering Lead specializing in Agentic AI enablement, you will lead the design and delivery of production-grade agent capabilities built on the enterprise AI Backbone across cloud and edge environments - across supply-chain and global functions. You will own end-to-end delivery of key agentic modules and integration patterns (MCP/tooling), establish strong evaluation and regression discipline, and drive adoption by embedding within transformation teams and BU and partnering with platform engineering and enterprise application owners. You serve as a technical anchor for the workstream—translating ambiguous business workflows into measurable agent outcomes, proactively identifying risks, proposing options/tradeoffs, and ensuring solutions scale across domains. Responsibilities BU Facing Solution Architecture & Implementation - (40%): Architect and deploy transformative AI agent solutions directly in client environments, adapting core technologies to unique client constraints and infrastructure. (Lead/Execute) Rapidly customize agent patterns (tool integrations, enterprise system connections, security models) to solve high-impact business challenges across diverse client tech stacks. (Lead/Execute) Transform ambiguous client requirements into production-ready solutions with minimal iterations through exceptional technical discovery skills. (Lead) Drive on-site performance optimization beyond client expectations (latency, reliability,throughput) while working within client infrastructure limitations. (Execute/Lead) Establish implementation playbooks that accelerate future deployments and enable customer success teams to scale. (Lead) Field-Based Quality Engineering & Diagnostics - (20%): Design and implement BU and function-specific evaluation frameworks that validate solution effectiveness in production environments with real data. (Lead/Execute) Develop rapid diagnostic methodologies to identify and resolve critical issues during implementation without disrupting client operations. (Execute/Lead) Create monitoring systems that provide early warning of edge cases and performance degradation unique to each deployment environment. (Execute) Perform advanced troubleshooting in constrained client environments where standard tools may be unavailable. (Execute/Lead) Establish quality baselines that enable clients to self-monitor system health post- implementation. (Execute/Lead) Function-Specific Model Optimization & Adaptation - (15%): Fine-tune model selection and routing strategies based on function-specific data characteristics and performance requirements. (Lead/Execute) Optimize prompt engineering for unique BU domains, creating specialized techniques that overcome domain-specific challenges. (Execute/Lead) Implement model adaptation techniques that improve performance with minimal additional client data. (Execute) Develop function-ready evaluation frameworks that demonstrate model effectiveness to technical and business stakeholders. (Lead) Enterprise Systems Integration & Data Flow Engineering - (15%): Lead complex integrations between AI capabilities and diverse client systems (ERPs, CRMs, legacy databases, custom applications). (Lead/Execute) Design and implement secure data pipelines that respect client compliance requirements while enabling AI functionality. (Execute/Lead) Create adapter patterns that isolate core AI functionality from client-specific integration complexities. (Lead) Develop client-specific documentation and knowledge transfer protocols that enable client teams to maintain integrations independently. (Execute/Lead) Client Success & Implementation Leadership -(10%): Serve as the primary technical bridge between core engineering and client stakeholders, translating between business needs and technical capabilities. (Lead) Mentor client technical teams to build internal AI implementation capabilities. (Lead) Drive adoption through hands-on workshops, knowledge transfer sessions, and executive-level capability demonstrations. (Lead/Execute) Identify expansion opportunities through deep understanding of client's technical landscape and business challenges. (Lead) Communicate complex technical concepts effectively to diverse audiences from C-suite to implementation teams. (Lead) Decision-Making Autonomy- High moderate Significant autonomy in AI engineering design choices and evaluation approach; aligns with standards and escalates policy/security-impacting decisions.

Supervision Required:

Moderate-low General direction from Transformation and Tech Executives and SME; self-directed execution with periodic design, execution and RoI reviews.

Complexity of Role:

High Spans agent design, evaluation rigor, integration complexity, and cross-team delivery and deep business/domain expertise under evolving constraints.

Cross-Functional Interactions:

Continuous interaction with domain transformation leads, platform/SRE, security, and enterprise app teams

Compensation and Benefits:

The expected compensation range for this position is between $123,500 - $206,750. Location, confirmed job-related skills, experience, and education will be considered in setting actual starting salary. Your recruiter can share more about the specific salary range during the hiring process. Bonus based on performance and eligibility target payout is 15% of annual salary paid out annually. Paid time off subject to eligibility, including paid parental leave, vacation, sick, and bereavement. In addition to salary, PepsiCo offers a comprehensive benefits package to support our employees and their families, subject to elections and eligibility: Medical, Dental, Vision, Disability, Health, and Dependent Care Reimbursement Accounts, Employee Assistance Program (EAP), Insurance (Accident, Group Legal, Life), Defined Contribution Retirement Plan.

Qualifications Minimum Qualifications:

Bachelor's in

CS/AI/ML

required or equivalent experience. Master's preferred. Expertise in ML (structured and unstructured data) development and engineering Proven experience shipping LLM/agent solutions to production with measurable quality and operational practices.

Required Expertise:

Advanced Software Engineering:

Python (and Java) mastery with distributed systems expertise; performance optimization (profiling,parallelization); architecture patterns (e.g., FastAPI, asyncio, Pydantic)

LLM & Agent Systems:

Multi-agent orchestration (LangChain, LangGraph, CrewAI); advanced prompt engineering; custom agent memory architectures; model optimization techniques

Evaluation Framework Development:

Statistical evaluation design (confidence intervals, power analysis); benchmark creation; instrumentation frameworks (e.g., MLflow, Arise); regression testing systems

ML Operations:

Production deployment pipelines (Docker, Kubernetes, Ray); model registry management; scaled inference optimization; GPU utilization optimization

System Architecture:

Microservice design patterns; high-throughput event processing; fault-tolerance implementation; horizontal scaling architectures

Technical Leadership:

Architecture governance systems; engineering standards development; build-vs-buy evaluation frameworks; technical roadmap creation

Adaptive Development:

Rapid prototyping; cross-platform implementation; client environment adaptation (air-gapped deployment, legacy system integration)

Field Implementation:

On-site deployment automation; custom agent development for specific domains; real-time system tuning; client-specific orchestration patterns

Function-Centric Evaluation:

Business KPI measurement frameworks; domain-specific benchmarking; hybrid test harnesses; real-world validation methodologies

Enterprise Integration:

Data Warehouse, Data Lake and Legacy system connectors (SAP, Oracle, Salesforce); secure data pipeline development; custom API wrapper creation; compliance-aware integration patterns

Infrastructure Adaptation:

On-premises AI deployment; private cloud implementation (Azure Stack, AWS Outposts); edge computing optimization; security-constrained architectures

Implementation Diagnostics:

Real-time debugging in production; performance profiling in restricted environments; root cause analysis methodologies; custom monitoring solutions

Security & Compliance:

Data tokenization techniques; compliance-aware architecture patterns; secure inference protocols; audit logging implementation

BU Success Engineering:

Technical documentation frameworks; knowledge transfer methodologies; executive-level technical demonstrations; BU/Function capability assessment

Good-to-have Skills:

Full-stack dev experience on modern stack Modelling User Interactions with AI Systems; Modeling multi-agent behaviour loops with tools like Temporal Agentic memory Patterns and usage with tools like MEM0 and Temporal Experience with Agentic RAG; Domain level Semantic Layer Designs with

Graph and Vector DBs Differentiating Competencies Required:

Ownership:

drives outcomes end-to-end for a workstream area (not just tasks) Collaboration & customer focus: influences stakeholders to deliver workflow value and adoption Communication & adaptability: executive-ready clarity on progress, risks, and evaluation evidence Proactiveness & initiative anticipates constraints, proposes options/tradeoffs early Strategic thinking: contributes to roadmap sequencing and reusable patterns across domains

Key Differentials:

Demonstrates exceptional adaptability to diverse technical environments and constraints Possesses rare combination of deep AI expertise and practical implementation experience across various industries Exhibits extraordinary problem-solving velocity in unfamiliar environments with incomplete information Maintains poise and technical excellence under pressure of client-facing deployment scenarios Balances innovation with pragmatic solution delivery in production environments Works effectively across organizational boundaries (our company, client IT, business stakeholders) Creates reusable patterns that accelerate future client implementations Serves as both technical expert and trusted advisor to client leadership Translates field learnings into product improvements that benefit all clients Thrives in ambiguous situations where requirements evolve during implementation EEO Statement Our Company will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the Fair Credit Reporting Act, and all other applicable laws, including but not limited to, San Francisco Police Code Sections 4901-4919, commonly referred to as the San Francisco Fair Chance Ordinance; and Chapter XVII, Article 9 of the Los Angeles Municipal Code, commonly referred to as the Fair Chance Initiative for Hiring Ordinance. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status. PepsiCo is an

Equal Opportunity Employer:

Female / Minority / Disability / Protected Veteran / Sexual Orientation / Gender Identity / Age If you'd like more information about your EEO rights as an applicant under the law, please download the available EEO is the Law & EEO is the Law Supplement documents. View PepsiCo EEO Policy . Please view our Pay Transparency Statement .

Qualifications:

Minimum Qualifications:

Bachelor s in

CS/AI/ML

required or equivalent experience. Master's preferred. Expertise in ML (structured and unstructured data) development and engineering Proven experience shipping LLM/agent solutions to production with measurable quality and operational practices.

Required Expertise:

Advanced Software Engineering:

Python (and Java) mastery with distributed systems expertise; performance optimization (profiling,parallelization); architecture patterns (e.g., FastAPI, asyncio, Pydantic)

LLM & Agent Systems:

Multi-agent orchestration (LangChain, LangGraph, CrewAI); advanced prompt engineering; custom agent memory architectures; model optimization techniques

Evaluation Framework Development:

Statistical evaluation design (confidence intervals, power analysis); benchmark creation; instrumentation frameworks (e.g., MLflow, Arise); regression testing systems

ML Operations:

Production deployment pipelines (Docker, Kubernetes, Ray); model registry management; scaled inference optimization; GPU utilization optimization

System Architecture:

Microservice design patterns; high-throughput event processing; fault-tolerance implementation; horizontal scaling architectures

Technical Leadership:

Architecture governance systems; engineering standards development; build-vs-buy evaluation frameworks; technical roadmap creation

Adaptive Development:

Rapid prototyping; cross-platform implementation; client environment adaptation (air-gapped deployment, legacy system integration)

Field Implementation:

On-site deployment automation; custom agent development for specific domains; real-time system tuning; client-specific orchestration patterns

Function-Centric Evaluation:

Business KPI measurement frameworks; domain-specific benchmarking; hybrid test harnesses; real-world validation methodologies

Enterprise Integration:

Data Warehouse, Data Lake and Legacy system connectors (SAP, Oracle, Salesforce); secure data pipeline development; custom API wrapper creation; compliance-aware integration patterns

Infrastructure Adaptation:

On-premises AI deployment; private cloud implementation (Azure Stack, AWS Outposts); edge computing optimization; security-constrained architectures

Implementation Diagnostics:

Real-time debugging in production; performance profiling in restricted environments; root cause analysis methodologies; custom monitoring solutions

Security & Compliance:

Data tokenization techniques; compliance-aware architecture patterns; secure inference protocols; audit logging implementation

BU Success Engineering:

Technical documentation frameworks; knowledge transfer methodologies; executive-level technical demonstrations; BU/Function capability assessment

Good-to-have Skills:

Full-stack dev experience on modern stack Modelling User Interactions with AI Systems; Modeling multi-agent behaviour loops with tools like Temporal Agentic memory Patterns and usage with tools like MEM0 and Temporal Experience with Agentic RAG; Domain level Semantic Layer Designs with

Graph and Vector DBs Differentiating Competencies Required:

Ownership:

drives outcomes end-to-end for a workstream area (not just tasks) Collaboration & customer focus: influences stakeholders to deliver workflow value and adoption Communication & adaptability: executive-ready clarity on progress, risks, and evaluation evidence Proactiveness & initiative anticipates constraints, proposes options/tradeoffs early Strategic thinking: contributes to roadmap sequencing and reusable patterns across domains

Key Differentials:

Demonstrates exceptional adaptability to diverse technical environments and constraints Possesses rare combination of deep AI expertise and practical implementation experience across various industries Exhibits extraordinary problem-solving velocity in unfamiliar environments with incomplete information Maintains poise and technical excellence under pressure of client-facing deployment scenarios Balances innovation with pragmatic solution delivery in production environments Works effectively across organizational boundaries (our company, client IT, business stakeholders) Creates reusable patterns that accelerate future client implementations Serves as both technical expert and trusted advisor to client leadership Translates field learnings into product improvements that benefit all clients Thrives in ambiguous situations where requirements evolve during implementation