Overview:

The AI Observability Architect is a senior technical leader responsible for designing, deploying, and operating an enterprise-grade, production-ready AI observability platform that spans the full spectrum of modern agentic AI — from large language model (LLM) workflows and multi-agent orchestration to physical AI systems, reinforcement learning harnesses, multi-modal pipelines, and agentic marketplaces. This role serves as the strategic and engineering authority for end-to-end telemetry, tracing, safety, and quality signals across heterogeneous agent frameworks and platforms.

 

The architect leads the convergence of AI observability with safety & security (including red teaming), Responsible AI (RAI), data science, physical AI, memory/skills engineering, agent fleet management, self-evolving harnesses, reinforcement learning, agent-to-agent protocols (A2A, UCP, AP2), and continuous quality engineering — making this a uniquely broad and high-impact role within the AI Solutions & Platforms organization.

 

The role also owns OpenTelemetry (OTEL) integration across third-party agentic platforms (Salesforce AgentForce, ServiceNow, Microsoft Agent 365, and others), enabling unified observability and governance at enterprise scale.

Responsibilities:

Agentic AI Observability Architecture at Scale  (30%)

• Define and own the enterprise observability architecture for AI agents, LLMs, multi-agent workflows, and physical AI systems — covering planner/executor loops, tool/function calls, RAG retrieval chains, and memory/state transitions.
• Build and operate unified telemetry pipelines incorporating metrics, logs, distributed traces, semantic/vector signals, and real-time event streaming (Kafka) at enterprise scale.
• Instrument OpenTelemetry (OTEL) across heterogeneous platforms including Salesforce AgentForce, ServiceNow, Microsoft Agent 365, and internal frameworks — delivering protocol-level observability for agent ecosystems including MCP, A2A, UCP, and AP2.
• Design and implement observability for Agent Fleets, multi-modal pipelines, physical AI systems, and self-evolving reinforcement learning harnesses — including signal capture for reward shaping and policy evaluation.
• Deliver dashboards, alerting, SLO/SLA management, incident runbook automation, and RCA tooling that drive measurable reliability improvements and reduce MTTR across agentic services.
• Establish cost telemetry and FinOps observability for AI workloads — token consumption, inference cost allocation, and GPU/compute efficiency across cloud environments (Azure, AWS, GCP).

Safety, Security & Red Teaming  (15%)

• Lead observability-driven red team exercises targeting agentic AI systems — instrumenting attack surfaces, adversarial prompt injection vectors, model evasion attempts, and multi-agent trust boundary failures.
• Design telemetry pipelines that capture safety-critical signals: guardrail trigger rates, policy violation events, PII exposure risks, prompt leakage, and agent hallucination rates.
• Partner with Security and RAI teams to embed threat modeling, zero-trust agent authentication, and behavioral anomaly detection into the observability platform.
• Instrument secure policy enforcement layers across agent-to-agent communication protocols (A2A, UCP, AP2) and maintain audit-ready traceability for all AI decision events.
• Develop and maintain a Security Observability Playbook covering incident classification, escalation paths, and forensic trace retention policies for agentic AI systems.

Responsible AI (RAI) & Governance  (10%)

• Integrate RAI signal capture — fairness, bias detection, explainability, and safety metrics — directly into observability pipelines, making compliance measurable and audit-ready.
• Deliver governance dashboards that surface RAI compliance posture across all active AI agents and LLM deployments, aligned with global regulatory standards.
• Support risk assessments, gap analyses, and governance frameworks with real-time observability insights — enabling proactive risk mitigation rather than reactive audit responses.
• Collaborate with RAI CoE and Legal/Compliance teams to define data retention, consent logging, and model decision traceability standards embedded in the telemetry architecture.

Quality Engineering for Agentic Solutions — Post Go-Live & Continuous QE  (10%)

• Own the Continuous Quality Engineering (CQE) framework for post-production agentic solutions — defining and tracking quality metrics across accuracy, latency, agent success rate, tool-call fidelity, and user outcome measures.
• Build automated quality gates within CI/CD pipelines that leverage observability data to detect regressions, drift, and degradation in agent performance — preventing silent failures in production.
• Instrument and monitor Skill Evaluations (evals) across the Memory, Skills, and MCP harness stack — providing traceability from eval results to production behavior.
• Partner with product and business stakeholders to define SLA-backed quality benchmarks and deliver automated alerting when quality thresholds are breached.
• Drive root-cause analysis for quality failures using distributed trace data, enabling rapid iteration and continuous improvement cycles for agentic solutions.

Memory, Skills, MCP & Harness Engineering Observability  (10%)

• Design and implement observability for the agent memory layer — episodic, semantic, and working memory read/write operations — providing latency, accuracy, and drift monitoring across memory backends.
• Instrument MCP (Model Context Protocol) server interactions, tool registrations, skill invocations, and context injection pipelines with full trace propagation and semantic tagging.
• Own observability for self-evolving harness and reinforcement learning (RL) systems — capturing reward signals, policy update events, environment state transitions, and learning convergence metrics.
• Monitor harness execution fidelity, skill eval pass/fail rates, and regression signals across training, fine-tuning, and inference workflows — feeding data back into the quality engineering loop.

Data Science Observability & Hardcore Python Engineering  (5%)

• Lead a team of senior Python engineers building high-performance, production-grade observability tooling — including custom OTEL exporters, semantic trace enrichers, signal aggregators, and anomaly detection pipelines.
• Apply data science methods — statistical process control, time-series anomaly detection, clustering, and causal inference — to transform raw telemetry into actionable AI operational intelligence.
• Build and maintain Python-native SDKs and libraries that simplify observability onboarding for agent developers across the organization.
• Establish code quality standards, testing frameworks, and peer review practices for the observability engineering team — embedding software craftsmanship into the team culture.

Agentic Marketplace, Registry & Ecosystem Observability  (5%)

• Instrument the Agentic Marketplace and Agent Registry platforms — providing usage telemetry, adoption metrics, capability health scores, and dependency mapping for registered agents and skills.
• Design observability APIs and SDK hooks that allow marketplace-registered agents to self-report health, performance, and behavioral signals into the central observability platform.
• Monitor inter-agent communication patterns across the marketplace ecosystem — identifying latency hotspots, circular dependencies, and protocol mismatches in agent-to-agent (A2A) workflows.
• Deliver a Marketplace Observability Dashboard surfacing agent catalog health, adoption trends, quality scores, and incident history — supporting marketplace governance and curation decisions.

Integration, Deployment & CI/CD Automation  (5%)

• Build and maintain CI/CD pipelines for observability services and agent operations center components, incorporating automated testing, deployment gates, and rollback mechanisms.
• Automate onboarding for new agent use cases using templates, scaffolding, and configuration validation — reducing time-to-observability from weeks to hours.
• Drive infrastructure-as-code (IaC) practices for observability platform components across Azure, AWS, and GCP — ensuring reproducible, version-controlled, and auditable deployments.

Product Delivery & Stakeholder Collaboration (10%)

• Operate with a product mindset — defining observability platform roadmaps, OKRs, adoption playbooks, and release milestones in partnership with AI platform and business teams.
• Collaborate with transformation teams, enterprise architects, security, and business stakeholders to tailor observability solutions to domain-specific requirements.
• Serve as the technical authority in executive and governance forums — translating complex observability data into business-relevant insights on risk, cost, and AI performance.
• Partner with SRE, AI platform, and product teams to drive standard adoption and reduce integration friction across the agentic AI ecosystem.

People Leadership & Team Development (5%)

• Build, mentor, and lead a high-performing observability engineering team — spanning Python developers, data scientists, and platform engineers — with talent initially based in India.
• Define career paths, skills development plans, and leveling criteria aligned with PepsiCo job architecture — fostering an inclusive, high-accountability team culture.
• Drive hiring, coaching, performance management, and succession planning across the observability function.

Decision-Making Autonomy

• High — Owns architecture decisions, platform roadmap, and engineering standards. Strategic alignment sought from AI Solutions Director on enterprise-level commitments.

Supervision Required

• Low to Moderate — Operates independently with periodic alignment reviews. Proactively escalates cross-organizational dependencies and risk trade-offs.

Role Complexity

• Very High — Spans observability, safety/security, RL harnesses, physical AI, multi-modal systems, agent protocols, quality engineering, and marketplace governance simultaneously

Compensation and Benefits:

• The expected compensation range for this position is between $123,500 - $206,750.
• 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 15% 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 Education & Experience:

• Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Software Engineering, or a related field (PhD a plus for research-heavy domains).
• 12+ years in technology with deep experience in enterprise observability, distributed systems, platform engineering, or AI/ML infrastructure.
• 5+ years in a senior/principal or architect-level role with demonstrated ownership of complex, cross-functional technical programs.

Core Technical Qualifications

• AI Observability & Distributed Systems: Expert-level knowledge of observability primitives (metrics, logs, traces, events) applied to LLM/ML/agentic systems; hands-on OpenTelemetry (OTEL) instrumentation including custom exporters, semantic conventions, and trace propagation across agent/tool boundaries.
• Agentic AI Frameworks: Direct experience with agentic AI platforms, multi-agent orchestration, LLM-based workflow design, and agent lifecycle management at production scale.
• Safety, Security & Red Teaming: Demonstrated experience conducting red team exercises against AI systems; knowledge of adversarial attack patterns, prompt injection, model evasion, and multi-agent trust boundary failures; ability to design safety telemetry pipelines.
• Memory, Skills & MCP: Working knowledge of agent memory architectures (episodic, semantic, working memory), Model Context Protocol (MCP), skill registries, and context injection patterns — with ability to design observability for these layers.
• Agent-to-Agent Protocols: Familiarity with A2A (Agent-to-Agent), UCP (Universal Communication Protocol), and AP2 patterns; ability to implement protocol-level observability and policy enforcement.
• Reinforcement Learning & Self-Evolving Harnesses: Understanding of RL training loops, reward signal capture, policy evaluation, and harness instrumentation for continuously improving agent systems.
• Physical AI & Multi-Modal Systems: Experience or strong familiarity with observability for physical AI pipelines (robotics, edge inference, sensor fusion) and multi-modal models (vision, audio, text).
• Data Science & Python Engineering: Proficiency in Python at a senior engineering level; experience with statistical anomaly detection, time-series analysis, and data pipeline design applied to observability data at scale.
• Platform Integrations (OTEL / Enterprise): Hands-on experience integrating OTEL with enterprise agentic platforms including Salesforce AgentForce, ServiceNow, Microsoft Agent 365, or similar; strong understanding of enterprise integration patterns and API design.
• Cloud & Infrastructure: Cloud fluency across Azure, AWS, and GCP; proficiency in Kubernetes, service mesh, IaC (Terraform/Bicep), and CI/CD tooling; experience with event streaming platforms (Kafka, Event Hubs).
• Quality Engineering for AI: Experience designing continuous quality frameworks (CQE) for agentic solutions including eval harnesses, regression detection, quality gates, and SLA-backed quality benchmarking.
• Responsible AI (RAI): Familiarity with RAI principles — fairness, bias detection, explainability, and safety — and ability to operationalize RAI signal capture within production observability pipelines.
• Agentic Marketplace & Registry: Experience or strong familiarity with agent marketplace architectures, capability registries, and platform governance — ideally with observability or monitoring responsibilities for marketplace-registered components.

Preferred / Differentiating Technical Skills

• Published contributions or hands-on experience with emerging agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, Bedrock Agents, or equivalent).
• Experience with Grafana, Datadog, New Relic, Dynatrace, or equivalent enterprise observability platforms — ideally extended to support AI/LLM workloads.
• Familiarity with vector databases (Pinecone, Weaviate, pgvector) and semantic search observability patterns relevant to RAG pipelines.
• Background in MLOps, LLMOps, or model lifecycle management — including model versioning, drift detection, and deployment governance.
• Experience designing observability APIs and SDK hooks for developer self-service onboarding.
  
  
• Differentiating Competencies Required - Translates enterprise AI strategy into observability architecture that simultaneously enables governance, safety, quality, and scale — holding the full picture across deeply technical and business dimensions.
• Safety-First Engineering Mindset - Instinctively designs systems with security, adversarial resilience, and RAI compliance as first-class requirements — not retrofitted features. Leads red team exercises with intellectual rigor and operational discipline.
• Outcome & Quality Orientation - Drives measurable impact: reduced MTTR, audit readiness, SLA adherence, agent quality scores, and RL harness convergence — translating telemetry data into business-relevant results.
• Cross-Functional Influencing - Navigates complex organizational dynamics — aligning engineering, governance, security, data science, and business units around shared observability standards and practices.
• Governance by Design - Integrates RAI, compliance, and security controls into design decisions from inception — producing systems that are audit-ready by default, not by remediation
• Technical Leadership Presence - Commands credibility in both executive and deep-technical forums; able to shift fluidly between C-suite communication and whiteboard architecture sessions with engineers.
• Adaptability & Continuous Learning - Thrives in a rapidly evolving AI landscape; quickly absorbs and operationalizes new frameworks, protocols, and research — from emerging agent communication standards to novel RL paradigms.
• Python Engineering Excellence - Holds a high bar for Python code quality, software craftsmanship, testing discipline, and developer experience — modeling best practices for the engineering team.

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