Lead AI Engineer

Forward Deployed Ai Engineering Lead

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.
• 12+ year experience with Software life cycle
• 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