AI Engineer

Overview

As an AI Engineer specializing in Agentic AI enablement, you will participate in 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 be responsible for 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 engine for the workstream—translating business workflows into measurable agent outcomes, working to mitigate identified risks, evaluating/experimenting with options/tradeoffs, and working to scale solutions across domains.

Responsibilities

Agent Engineering & Workstream Delivery (35%)

• Lead design and productionization of high-leverage agent modules and reusable patterns (tool-use orchestration, policies/guardrails, memory, RAG where it adds measurable value), built as composable components and reference implementations. *(Execute/Lead)*
• Translate ambiguous product/problem statements into concrete agent behaviors and system designs: state models, failure modes, tool contracts, latency budgets, and acceptance criteria that engineering + product can execute against. *(Execute/Consult)*
• Deliver quickly without sacrificing quality: create thin vertical slices, iterate with evidence, and converge on robust behavior under real-world constraints. *(Execute)*
• Drive meaningful performance gains via systematic optimization: latency, token efficiency, tool-call success, retrieval quality, and cost per successful task, including remediation of long-tail failure modes. *(Execute)*
• Proactively identify platformizable opportunities: refactor one-off implementations into shared frameworks/SDKs that reduce build time for others. *(Execute/Influence)*

Evaluation, Testing & Release Quality (25%)

• Define and implement evaluation strategies for assigned workflows: golden sets, scenario coverage maps, regression suites, online/offline metrics, and release gating thresholds aligned to real business outcomes. *(Execute/Consult)*
• Build repeatable evaluation systems (templates, labeling guidance, dataset/versioning conventions, dashboards/reports) so evaluation becomes a productized capability, not ad hoc testing. *(Execute/Lead)*
• Implement robust automated testing across layers: unit tests for prompt/tool wrappers, contract tests for tool schemas, integration tests for toolchains, and agent simulation tests for multi-step flows. *(Execute)*
• Lead root-cause analysis of quality failures (hallucinations, tool misuse, retrieval misses, routing errors): isolate causes (prompt/tool/data/model), implement corrective actions, and prevent regressions. *(Execute)*
• Champion evidence-first iteration: decisions and releases are backed by eval results, not gut feel. *(Influence)*

Model/Prompt Routing Contributions (15%)

• Contribute to router design and task-to-model mapping through routing rules/classifiers, prompt strategies, and model selection policies; validate decisions using evaluation data and runtime telemetry. *(Execute/Consult)*
• Propose and implement routing improvements when constraints change (pricing, latency, throughput, new model capabilities), with governance-aware rollouts and rollback plans. *(Consult/Execute)*
• Identify and mitigate routing failure modes (over-escalation to expensive models, under-routing causing quality loss, brittle heuristics) and improve robustness using lightweight ML or rules where appropriate. *(Execute)*

Integration with Tools and MCPs (15%)

• Lead implementation of MCP connectors/clients for enterprise apps and internal data products with strong engineering hygiene: schema/versioning discipline, typed contracts, scopes/permissions, auditability, and integration test strategy. *(Execute/Consult)*
• Build reusable integration patterns: standardized tool metadata, error normalization, retries/timeouts, idempotency, pagination handling, and consistent auth patterns to accelerate onboarding of new tools. *(Execute)*
• Collaborate with security/data owners to ensure secure-by-design tool access (least privilege, logging, PII handling, policy enforcement). *(Consult/Execute)*

Operational Readiness, Collaboration & Continuous Improvement (10%)

• Ensure production readiness for owned components: telemetry coverage, structured logging, traceability for tool calls, SLIs/SLO alignment (latency, success rate, cost), and participation in incident response and postmortems. *(Execute/Consult)*
• Proactively identify delivery risks (dependencies, rate limits, data quality, security scopes, vendor constraints) and drive resolution with clear tradeoffs and recommendations. *(Consult/Influence)*
• Mentor peers through technical leadership: raise code quality, share patterns, review PRs for correctness/performance/security, and contribute to internal playbooks. *(Influence)*

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 $93,500 - $156,450.
• 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 10% 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
• 6-8 year experience in 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
• Enterprise Integration: Enterprise connector development; scalable API architectures; data pipeline engineering (Kafka, gRPC, Redis); authorization protocol implementation
• Observability Engineering: Telemetry system design (Prometheus, OpenTelemetry); automated anomaly detection; distributed tracing; performance dashboarding (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: providing clarity on progress, risks, and evaluation evidence to business, technical and PMO stakeholders
• 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 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

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.

Minimum Qualifications

• Bachelor’s in CS/AI/ML or equivalent experience required
• Master’s preferred
• 6-8 year experience in 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
• Enterprise Integration: Enterprise connector development; scalable API architectures; data pipeline engineering (Kafka, gRPC, Redis); authorization protocol implementation
• Observability Engineering: Telemetry system design (Prometheus, OpenTelemetry); automated anomaly detection; distributed tracing; performance dashboarding (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: providing clarity on progress, risks, and evaluation evidence to business, technical and PMO stakeholders
• 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 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

Agent Engineering & Workstream Delivery (35%)

• Lead design and productionization of high-leverage agent modules and reusable patterns (tool-use orchestration, policies/guardrails, memory, RAG where it adds measurable value), built as composable components and reference implementations. *(Execute/Lead)*
• Translate ambiguous product/problem statements into concrete agent behaviors and system designs: state models, failure modes, tool contracts, latency budgets, and acceptance criteria that engineering + product can execute against. *(Execute/Consult)*
• Deliver quickly without sacrificing quality: create thin vertical slices, iterate with evidence, and converge on robust behavior under real-world constraints. *(Execute)*
• Drive meaningful performance gains via systematic optimization: latency, token efficiency, tool-call success, retrieval quality, and cost per successful task, including remediation of long-tail failure modes. *(Execute)*
• Proactively identify platformizable opportunities: refactor one-off implementations into shared frameworks/SDKs that reduce build time for others. *(Execute/Influence)*

Evaluation, Testing & Release Quality (25%)

• Define and implement evaluation strategies for assigned workflows: golden sets, scenario coverage maps, regression suites, online/offline metrics, and release gating thresholds aligned to real business outcomes. *(Execute/Consult)*
• Build repeatable evaluation systems (templates, labeling guidance, dataset/versioning conventions, dashboards/reports) so evaluation becomes a productized capability, not ad hoc testing. *(Execute/Lead)*
• Implement robust automated testing across layers: unit tests for prompt/tool wrappers, contract tests for tool schemas, integration tests for toolchains, and agent simulation tests for multi-step flows. *(Execute)*
• Lead root-cause analysis of quality failures (hallucinations, tool misuse, retrieval misses, routing errors): isolate causes (prompt/tool/data/model), implement corrective actions, and prevent regressions. *(Execute)*
• Champion evidence-first iteration: decisions and releases are backed by eval results, not gut feel. *(Influence)*

Model/Prompt Routing Contributions (15%)

• Contribute to router design and task-to-model mapping through routing rules/classifiers, prompt strategies, and model selection policies; validate decisions using evaluation data and runtime telemetry. *(Execute/Consult)*
• Propose and implement routing improvements when constraints change (pricing, latency, throughput, new model capabilities), with governance-aware rollouts and rollback plans. *(Consult/Execute)*
• Identify and mitigate routing failure modes (over-escalation to expensive models, under-routing causing quality loss, brittle heuristics) and improve robustness using lightweight ML or rules where appropriate. *(Execute)*

Integration with Tools and MCPs (15%)

• Lead implementation of MCP connectors/clients for enterprise apps and internal data products with strong engineering hygiene: schema/versioning discipline, typed contracts, scopes/permissions, auditability, and integration test strategy. *(Execute/Consult)*
• Build reusable integration patterns: standardized tool metadata, error normalization, retries/timeouts, idempotency, pagination handling, and consistent auth patterns to accelerate onboarding of new tools. *(Execute)*
• Collaborate with security/data owners to ensure secure-by-design tool access (least privilege, logging, PII handling, policy enforcement). *(Consult/Execute)*

Operational Readiness, Collaboration & Continuous Improvement (10%)

• Ensure production readiness for owned components: telemetry coverage, structured logging, traceability for tool calls, SLIs/SLO alignment (latency, success rate, cost), and participation in incident response and postmortems. *(Execute/Consult)*
• Proactively identify delivery risks (dependencies, rate limits, data quality, security scopes, vendor constraints) and drive resolution with clear tradeoffs and recommendations. *(Consult/Influence)*
• Mentor peers through technical leadership: raise code quality, share patterns, review PRs for correctness/performance/security, and contribute to internal playbooks. *(Influence)*

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 $93,500 - $156,450.
• 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 10% 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.