Lead AI Engineer

PepsiCo • Town/Village of Harrison, NY

19h • Onsite

About The Position

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.

Requirements

• Bachelor’s in CS/AI/ML required or equivalent experience.
• Expertise in ML (structured and unstructured data) development and engineering
• Proven experience shipping LLM/agent solutions to production with measurable quality and operational practices.
• 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
• 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
• 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

Nice To Haves

• Master's preferred.
• 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

Responsibilities

• Architect and deploy transformative AI agent solutions directly in client environments, adapting core technologies to unique client constraints and infrastructure.
• Rapidly customize agent patterns (tool integrations, enterprise system connections, security models) to solve high-impact business challenges across diverse client tech stacks.
• Transform ambiguous client requirements into production-ready solutions with minimal iterations through exceptional technical discovery skills.
• Drive on-site performance optimization beyond client expectations (latency, reliability, throughput) while working within client infrastructure limitations.
• Establish implementation playbooks that accelerate future deployments and enable customer success teams to scale.
• Design and implement BU and function-specific evaluation frameworks that validate solution effectiveness in production environments with real data.
• Develop rapid diagnostic methodologies to identify and resolve critical issues during implementation without disrupting client operations.
• Create monitoring systems that provide early warning of edge cases and performance degradation unique to each deployment environment.
• Perform advanced troubleshooting in constrained client environments where standard tools may be unavailable.
• Establish quality baselines that enable clients to self-monitor system health post-implementation.
• Fine-tune model selection and routing strategies based on function-specific data characteristics and performance requirements.
• Optimize prompt engineering for unique BU domains, creating specialized techniques that overcome domain-specific challenges.
• Implement model adaptation techniques that improve performance with minimal additional client data.
• Develop function-ready evaluation frameworks that demonstrate model effectiveness to technical and business stakeholders.
• Lead complex integrations between AI capabilities and diverse client systems (ERPs, CRMs, legacy databases, custom applications).
• Design and implement secure data pipelines that respect client compliance requirements while enabling AI functionality.
• Create adapter patterns that isolate core AI functionality from client-specific integration complexities.
• Develop client-specific documentation and knowledge transfer protocols that enable client teams to maintain integrations independently.
• Serve as the primary technical bridge between core engineering and client stakeholders, translating between business needs and technical capabilities.
• Mentor client technical teams to build internal AI implementation capabilities.
• Drive adoption through hands-on workshops, knowledge transfer sessions, and executive-level capability demonstrations.
• Identify expansion opportunities through deep understanding of client's technical landscape and business challenges.
• Communicate complex technical concepts effectively to diverse audiences from C-suite to implementation teams.

Benefits

• 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.
• Medical, Dental, Vision, Disability, Health, and Dependent Care Reimbursement Accounts, Employee Assistance Program (EAP), Insurance (Accident, Group Legal, Life), Defined Contribution Retirement Plan.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior