AI Solutions Assoc Prin Engineer

Overview CAREERS TO SMILE ABOUT At PepsiCo, you’ll discover a place where our mission is to create smiles around the world. With a portfolio of more than 500 beloved brands including, Gatorade, Lay’s and Quaker, our work touches millions of people every day. At the heart of the company is a team of thinkers, creators, and problem-solvers who collaborate to innovate and turn ideas into action. Driven by innovation and a focus on creating joyful moments through food and drinks, our decisions are guided by consumer centricity, creating opportunities for our associates to do meaningful work and make a lasting impact in the communities we serve. Whatever your role, you’ll be part of a global community that values your ideas and empowers you to make an impact, on your career and on the world around you. Responsibilities Platform Reference Architecture & Roadmap (30%) Define and maintain the end to end PepGenX platform reference architecture across agent lifecycle, orchestration, runtime, gateway, tools/connectors, memory/state, multi tenant isolation, and environment management. Establish platform capability standards (interfaces, versioning, compatibility) to enable safe reuse across teams and domains. Own the platform roadmap including release sequencing, dependency management, and deprecation strategy. Ensure architecture choices support scale, portability, and consistent developer experience. Security, Identity & Policy Enforcement (20%) Define secure by design patterns for non human identity, credential handling, and least privilege access across agent workflows and tools. Establish RBAC/ABAC authorization models and consent/entitlement propagation across system boundaries. Enforce Explicit Boundary Enforcement (EBE) so cross system and cross agent access occurs only through governed contracts and platform controls. Standardize policy enforcement points (gateway, tool contracts, orchestration) rather than relying on prompt instructions. Auditability, Data Protection & Compliance Controls (15%) Define audit evidence requirements for agent actions, tool calls, approvals/handoffs, prompt/template changes, and model/provider switching. Establish standards for data classification, PII handling/redaction, and retention/purge policies for prompts, traces, and outputs. Define policy based routing to compliant models/providers based on data sensitivity and region/tenant requirements. Ensure compliance controls are measurable and verifiable through logs/traces and operational evidence. Enterprise Integration Standards & Tool Contracts (15%) Define standard integration patterns with enterprise platforms (e.g., SAP, ServiceNow, Salesforce) through gateway and governed interfaces. Establish tool/API contract standards including schemas, error semantics, timeouts, retries, and idempotency expectations. Define standards for A2A/MCP usage, trust boundaries, and capability contracts across agents and tools. Drive interoperability and portability across cloud/runtime environments through consistent integration abstractions. Deterministic Human-Agent Collaboration & Long Running Orchestration (10%) Define deterministic human-agent collaboration patterns (assign, escalate, co pilot, co author) with clear approval and escalation gates. Establish long running orchestration patterns including timers, retries, idempotency, and compensation/rollback for safe execution. Standardize workflow state handling and durable context management across multi step enterprise processes. Ensure orchestration patterns support safe failure recovery and prevent duplicate side effects. Platform performance, Observability, CI/CD Gates & Adoption Enablement (10%) Define platform SLO posture and operational readiness standards, including incident and credential compromise runbooks. Standardize observability requirements via PepVigil (logs, metrics, traces, evals, and cost) for platform and onboarded workloads. Define CI/CD promotion gates including versioning, testing, security scans, approvals, and drift detection. Publish reference implementations, onboarding playbooks, and enablement materials to accelerate adoption and reduce bespoke delivery. Decision-Making Autonomy Minimum – Work with Senior Platform Architect 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 Yes – 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 $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 Education: Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related field. 9+ years in architecture / platform engineering, with experience designing enterprise platforms Experience: Proven experience in architecting AI Platform and solution with hands-on experience in code development and delivering production systems with security, governance, and operability Proven experience taking platforms from design, build ‚ operate, including governance, production support, and multi-team adoption. Required Expertise: Proficiency in programming languages such as Python, Java, or C++. Platform architecture for distributed systems; multi‑tenant design and environment isolation. Solid understanding and hands on knowledge of temporal, Cloud and open source agent runtime, registry and gateway services Demonstrated ability to define and maintain reference architectures, reusable standards, and "golden path" patterns, and to own a multi-release platform roadmap Strong experience designing and enforcing security-by-design patterns, including non-human identity, credential lifecycle management, least-privilege access, and enterprise authorization approaches (e.g., RBAC/ABAC). Strong integration architecture background, including API gateway-based integration patterns and contract-driven interfaces; experience integrating with large enterprise platforms such as SAP, ServiceNow, and/or Salesforce (or equivalent systems of record). Experience designing and operating long-running orchestration and human-in-the-loop workflows with reliable retry, idempotency, and compensation/rollback patterns. Strong operational readiness mindset, including defining SLOs, observability standards (logs/metrics/traces), CI/CD gates (testing, security scans, approvals), and production support runbooks. 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 technologie 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. Platform Reference Architecture & Roadmap (30%) Define and maintain the end to end PepGenX platform reference architecture across agent lifecycle, orchestration, runtime, gateway, tools/connectors, memory/state, multi tenant isolation, and environment management. Establish platform capability standards (interfaces, versioning, compatibility) to enable safe reuse across teams and domains. Own the platform roadmap including release sequencing, dependency management, and deprecation strategy. Ensure architecture choices support scale, portability, and consistent developer experience. Security, Identity & Policy Enforcement (20%) Define secure by design patterns for non human identity, credential handling, and least privilege access across agent workflows and tools. Establish RBAC/ABAC authorization models and consent/entitlement propagation across system boundaries. Enforce Explicit Boundary Enforcement (EBE) so cross system and cross agent access occurs only through governed contracts and platform controls. Standardize policy enforcement points (gateway, tool contracts, orchestration) rather than relying on prompt instructions. Auditability, Data Protection & Compliance Controls (15%) Define audit evidence requirements for agent actions, tool calls, approvals/handoffs, prompt/template changes, and model/provider switching. Establish standards for data classification, PII handling/redaction, and retention/purge policies for prompts, traces, and outputs. Define policy based routing to compliant models/providers based on data sensitivity and region/tenant requirements. Ensure compliance controls are measurable and verifiable through logs/traces and operational evidence. Enterprise Integration Standards & Tool Contracts (15%) Define standard integration patterns with enterprise platforms (e.g., SAP, ServiceNow, Salesforce) through gateway and governed interfaces. Establish tool/API contract standards including schemas, error semantics, timeouts, retries, and idempotency expectations. Define standards for A2A/MCP usage, trust boundaries, and capability contracts across agents and tools. Drive interoperability and portability across cloud/runtime environments through consistent integration abstractions. Deterministic Human-Agent Collaboration & Long Running Orchestration (10%) Define deterministic human-agent collaboration patterns (assign, escalate, co pilot, co author) with clear approval and escalation gates. Establish long running orchestration patterns including timers, retries, idempotency, and compensation/rollback for safe execution. Standardize workflow state handling and durable context management across multi step enterprise processes. Ensure orchestration patterns support safe failure recovery and prevent duplicate side effects. Platform performance, Observability, CI/CD Gates & Adoption Enablement (10%) Define platform SLO posture and operational readiness standards, including incident and credential compromise runbooks. Standardize observability requirements via PepVigil (logs, metrics, traces, evals, and cost) for platform and onboarded workloads. Define CI/CD promotion gates including versioning, testing, security scans, approvals, and drift detection. Publish reference implementations, onboarding playbooks, and enablement materials to accelerate adoption and reduce bespoke delivery. Decision-Making Autonomy Minimum – Work with Senior Platform Architect 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 Yes – 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 $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. Education: Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related field. 9+ years in architecture / platform engineering, with experience designing enterprise platforms Experience: Proven experience in architecting AI Platform and solution with hands-on experience in code development and delivering production systems with security, governance, and operability Proven experience taking platforms from design, build ‚ operate, including governance, production support, and multi-team adoption. Required Expertise: Proficiency in programming languages such as Python, Java, or C++. Platform architecture for distributed systems; multi‑tenant design and environment isolation. Solid understanding and hands on knowledge of temporal, Cloud and open source agent runtime, registry and gateway services Demonstrated ability to define and maintain reference architectures, reusable standards, and "golden path" patterns, and to own a multi-release platform roadmap Strong experience designing and enforcing security-by-design patterns, including non-human identity, credential lifecycle management, least-privilege access, and enterprise authorization approaches (e.g., RBAC/ABAC). Strong integration architecture background, including API gateway-based integration patterns and contract-driven interfaces; experience integrating with large enterprise platforms such as SAP, ServiceNow, and/or Salesforce (or equivalent systems of record). Experience designing and operating long-running orchestration and human-in-the-loop workflows with reliable retry, idempotency, and compensation/rollback patterns. Strong operational readiness mindset, including defining SLOs, observability standards (logs/metrics/traces), CI/CD gates (testing, security scans, approvals), and production support runbooks. 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 technologie