Senior AI Engineer

Senior AI Engineer Specializing In Agentic AI Enablement As a Senior AI Engineer 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 agent modules and integration patterns (MCP/tooling), establish strong evaluation and regression discipline, and drive adoption by partnering with transformation teams, BU, 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 Architectural Leadership & Strategic Execution (40%) Design and architect transformative agent systems that enable organization-wide scaling, establishing new paradigms in agent architecture that become company standards. Pioneer novel agent patterns (tool-use orchestration, multi-agent systems, advanced memory architectures) that dramatically improve performance across the enterprise. Transform ambiguous business problems into elegant technical solutions with 10x efficiency gains through innovative approaches to system design. Optimize critical performance metrics beyond standard benchmarks, creating breakthrough improvements (90th percentile latency reduction, 50%+ token efficiency, near-perfect tool-call reliability). Establish architectural governance that propagates excellence across teams and projects. Advanced Evaluation & Quality Engineering (20%) Design scientifically rigorous evaluation frameworks that uncover non-obvious failure modes and edge cases others miss. Create organization-level evaluation standards and platforms that scale across multiple teams and projects. Innovate on automated testing methodologies that dramatically increase code quality while reducing QA overhead. Perform sophisticated statistical analysis of system behaviors to predict quality issues before they manifest. Establish early warning systems for emerging failure patterns. Model Architecture & Routing Innovation (15%) Architect intelligent routing systems that autonomously optimize for cost, latency, and quality trade-offs. Pioneer novel approaches to model selection, fine-tuning, and prompt engineering that set new performance standards. Create optimization algorithms that continuously improve routing decisions based on real-time feedback loops. Develop proprietary techniques for model evaluation that provide competitive advantage. Advanced Integration & Ecosystem Development (15%) Design scalable integration architectures that become enterprise standards for AI/app connectivity. Create abstraction layers that dramatically simplify how teams connect AI capabilities to enterprise systems. Establish next-generation integration patterns that anticipate future technology directions and enable seamless adoption. Develop tooling that accelerates integration velocity across the entire organization. Organizational Multiplier & Innovation Leadership (10%) Serve as technical visionary, elevating the entire AI organization's capabilities through knowledge transfer and mentorship. Anticipate industry shifts and position the organization to capitalize on emerging technological opportunities. Create internal communities of practice that accelerate knowledge sharing and collective innovation. Represent the company's technical excellence externally through publications, speaking engagements, and industry contributions. Drive cross-functional initiatives that break down silos and create new organizational capabilities. 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: Yes — 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 $110,700 - $185,250. 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 12% 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 or equivalent experience required Master's preferred 8+ 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 (e.g., 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 (e.g., Docker, Kubernetes, Ray); model registry management; scaled inference optimization; GPU utilization optimization Enterprise Integration: Enterprise connector development; scalable API architectures; data pipeline engineering (e.g., Kafka, gRPC, Redis); authorization protocol implementation Observability Engineering: Telemetry system design (e.g., Prometheus, OpenTelemetry); automated anomaly detection; distributed tracing; performance dashboarding (e.g., Grafana) 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 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 proven history of creating solutions with order-of-magnitude improvements over standard approaches Possesses rare combination of deep technical expertise and strategic business understanding Creates solutions that scale beyond their direct involvement (leveraged impact) Consistently elevates the performance of teams and individuals around them Identifies and solves problems others haven't recognized yet Maintains extraordinary productivity while ensuring knowledge transfer Balances technical perfectionism with pragmatic business value Communicates complex technical concepts effectively to both technical and non-technical stakeholders PepsiCo