AI Solutions Principal Engineer

Overview AI Solutions Principal Engineer Responsibilities Solution Architecture & Design - (40%) Produce end-to-end solution architecture artifacts (HLD/LLD, C4 context/container, sequence diagrams) for consumer/commercial agentic use cases and enabling services. Define integration patterns with customer-facing platforms (CRM, commerce, marketing tech, customer data platforms), API ecosystems, and data platforms. Governance & Architecture Review - (25%) Review agentic solution designs for compliance with platform patterns, security guardrails, and enterprise architecture standards. Document ADRs for key design choices, exceptions, and trade-offs; drive remediation actions for non-compliant designs. Non‑Functional & Production Readiness - (20%) Define and validate NFRs: availability, latency, throughput, resilience, auditability, security, privacy, and cost controls. Ensure observability and operational readiness are designed-in (logging, tracing, metrics, evals, runbooks, rollback patterns). Stakeholder Collaboration - (15%) Partner with product owners, engineering teams, data teams, platform teams, and vendors to deliver aligned architecture and unblock dependencies. Facilitate architecture workshops and design sign-offs across cross-functional teams. Decision-Making Autonomy - Moderate Significant autonomy in the technical aspects of AI model development and implementation, working under the strategic direction provided by the Senior AI Solutions Manager. Supervision Required - Moderate Operates with general guidance from the Senior AI Solutions Manager, with regular updates for alignment and support. Complexity of Role - High The role requires managing complex AI/ML projects, working with large datasets, and ensuring successful integration with existing systems while maintaining scalability. Cross-Functional Interactions Regular interaction with Data Science, Engineering, IT, digital products and business stakeholders to ensure effective AI solution deployment. 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 Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Data/AI, or related field. Experience: 8–12 years in solution architecture with consumer/commercial digital platform exposure. Required Skills / Experience: Proven experience producing solution architectures for enterprise initiatives, including HLD/LLD, architecture diagrams, interface specifications, and NFRs. Strong integration architecture experience across APIs, event-driven patterns, data products, and customer platform ecosystems. Practical understanding of agentic AI architecture: tool/action execution, orchestration, memory/state, retrieval/grounding, evaluation/quality, and human-in-the-loop controls. Experience designing for security, privacy, and compliance in customer-facing contexts: IAM/RBAC, data classification/PII, consent considerations, auditability, and explicit trust boundary enforcement. Experience with production readiness: observability (logs/metrics/traces), resilience (timeouts/retries/idempotency), runbooks, and controlled rollout/rollback patterns. Strong understanding of consumer/commercial journeys and systems (at least 3–4): marketing workflows, sales processes, commerce checkout/order flows, customer identity, B2B portals, or D2C experiences. Proficiency in programming languages such as Python, Java, or C++. Ability to drive adoption through clear documentation, reference implementations, and enablement of engineers and architects across teams. Experience and working knowledge with Agentic AI frameworks (e.g., Langchain, CrewAi, MCP, A2A) and deployment of AI solutions on cloud infrastructures (AWS, Azure, or Google Cloud). Strong understanding and experience in designing AI agents and integrating advancements in AI/ML technologies. Differentiating Competencies Strong stakeholder influence and ability to drive alignment across product, engineering, data, and security teams. Ability to simplify complex systems into clear architectures and guide teams toward reusable patterns. Bias for measurable outcomes: reliability, adoption, cost, and operational stability. 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. Solution Architecture & Design - (40%) Produce end-to-end solution architecture artifacts (HLD/LLD, C4 context/container, sequence diagrams) for consumer/commercial agentic use cases and enabling services. Define integration patterns with customer-facing platforms (CRM, commerce, marketing tech, customer data platforms), API ecosystems, and data platforms. Governance & Architecture Review - (25%) Review agentic solution designs for compliance with platform patterns, security guardrails, and enterprise architecture standards. Document ADRs for key design choices, exceptions, and trade-offs; drive remediation actions for non-compliant designs. Non‑Functional & Production Readiness - (20%) Define and validate NFRs: availability, latency, throughput, resilience, auditability, security, privacy, and cost controls. Ensure observability and operational readiness are designed-in (logging, tracing, metrics, evals, runbooks, rollback patterns). Stakeholder Collaboration - (15%) Partner with product owners, engineering teams, data teams, platform teams, and vendors to deliver aligned architecture and unblock dependencies. Facilitate architecture workshops and design sign-offs across cross-functional teams. Decision-Making Autonomy - Moderate Significant autonomy in the technical aspects of AI model development and implementation, working under the strategic direction provided by the Senior AI Solutions Manager. Supervision Required - Moderate Operates with general guidance from the Senior AI Solutions Manager, with regular updates for alignment and support. Complexity of Role - High The role requires managing complex AI/ML projects, working with large datasets, and ensuring successful integration with existing systems while maintaining scalability. Cross-Functional Interactions Regular interaction with Data Science, Engineering, IT, digital products and business stakeholders to ensure effective AI solution deployment. 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. Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Data/AI, or related field. Experience: 8–12 years in solution architecture with consumer/commercial digital platform exposure. Required Skills / Experience: Proven experience producing solution architectures for enterprise initiatives, including HLD/LLD, architecture diagrams, interface specifications, and NFRs. Strong integration architecture experience across APIs, event-driven patterns, data products, and customer platform ecosystems. Practical understanding of agentic AI architecture: tool/action execution, orchestration, memory/state, retrieval/grounding, evaluation/quality, and human-in-the-loop controls. Experience designing for security, privacy, and compliance in customer-facing contexts: IAM/RBAC, data classification/PII, consent considerations, auditability, and explicit trust boundary enforcement. Experience with production readiness: observability (logs/metrics/traces), resilience (timeouts/retries/idempotency), runbooks, and controlled rollout/rollback patterns. Strong understanding of consumer/commercial journeys and systems (at least 3–4): marketing workflows, sales processes, commerce checkout/order flows, customer identity, B2B portals, or D2C experiences. Proficiency in programming languages such as Python, Java, or C++. Ability to drive adoption through clear documentation, reference implementations, and enablement of engineers and architects across teams. Experience and working knowledge with Agentic AI frameworks (e.g., Langchain, CrewAi, MCP, A2A) and deployment of AI solutions on cloud infrastructures (AWS, Azure, or Google Cloud). Strong understanding and experience in designing AI agents and integrating advancements in AI/ML technologies. Differentiating Competencies Strong stakeholder influence and ability to drive alignment across product, engineering, data, and security teams. Ability to simplify complex systems into clear architectures and guide teams toward reusable patterns. Bias for measurable outcomes: reliability, adoption, cost, and operational stability.