7 Pepsico Options Trader Jobs Hiring Near You

Overview
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. (Lead/Execute)
• Pioneer novel agent patterns (tool-use orchestration, multi-agent systems, advanced memory architectures) that dramatically improve performance across the enterprise. (Lead/Execute)
• Transform ambiguous business problems into elegant technical solutions with 10x efficiency gains through innovative approaches to system design. (Lead)
• Optimize critical performance metrics beyond standard benchmarks, creating breakthrough improvements (90th percentile latency reduction, 50%+ token efficiency, near-perfect tool-call reliability). (Execute/Lead)
• Establish architectural governance that propagates excellence across teams and projects. (Lead)

Advanced Evaluation & Quality Engineering (20%)

• Design scientifically rigorous evaluation frameworks that uncover non-obvious failure modes and edge cases others miss. (Lead/Execute)
• Create organization-level evaluation standards and platforms that scale across multiple teams and projects. (Lead)
• Innovate on automated testing methodologies that dramatically increase code quality while reducing QA overhead. (Execute/Lead)
• Perform sophisticated statistical analysis of system behaviors to predict quality issues before they manifest. (Execute)
• Establish early warning systems for emerging failure patterns. (Execute/Lead)

Model Architecture & Routing Innovation (15%)

• Architect intelligent routing systems that autonomously optimize for cost, latency, and quality trade-offs. (Lead/Execute)
• Pioneer novel approaches to model selection, fine-tuning, and prompt engineering that set new performance standards. (Lead)
• Create optimization algorithms that continuously improve routing decisions based on real-time feedback loops. (Execute/Lead)
• Develop proprietary techniques for model evaluation that provide competitive advantage. (Execute/Lead)

Advanced Integration & Ecosystem Development (15%)

• Design scalable integration architectures that become enterprise standards for AI/app connectivity. (Lead)
• Create abstraction layers that dramatically simplify how teams connect AI capabilities to enterprise systems. (Execute/Lead)
• Establish next-generation integration patterns that anticipate future technology directions and enable seamless adoption. (Lead)
• Develop tooling that accelerates integration velocity across the entire organization. (Execute/Lead)

Organizational Multiplier & Innovation Leadership (10%)

• Serve as technical visionary, elevating the entire AI organization's capabilities through knowledge transfer and mentorship. (Lead)
• Anticipate industry shifts and position the organization to capitalize on emerging technological opportunities. (Lead)
• Create internal communities of practice that accelerate knowledge sharing and collective innovation. (Lead)
• Represent the company's technical excellence externally through publications, speaking engagements, and industry contributions. (Lead)
• Drive cross-functional initiatives that break down silos and create new organizational capabilities. (Lead/Execute)

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

• Identify any differentiating behaviors, leadership skills or soft skills required for success in the role.
• 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

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