Note: The job is a remote job and is open to candidates in USA. Zeta Global is an AI-powered marketing cloud company that uses artificial intelligence and consumer signals to help marketers acquire, grow, and retain customers. The Principal Product Manager will lead product architecture and strategy for agentic systems, including agent orchestration, context architecture, telemetry, evaluation, model workbench development, and platform governance. The role requires cross-functional alignment, technical troubleshooting, rapid prototyping, and development of scalable AI platform capabilities.
Responsibilities
- Agent Chaining and Orchestration: Develop and manage the architecture for chaining LLM agents, tools, models, and workflows across complex use cases. Ensure seamless orchestration, handoffs, state management, and integration across the platform
- Context Graph and Context Architecture: Lead the development of a shared Context Graph that gives agents persistent awareness of users, brands, accounts, workflows, capabilities, data, prior actions, goals, and outcomes. Define how context is captured, structured, retrieved, governed, and made available across agents and products
- Context Streaming Services: Implement and manage context streaming services that provide agents with real-time awareness of user actions, application state, system events, and relevant business data. Ensure context remains current, permission-aware, and usable across multi-step workflows
- Agent Telemetry and Observability: Define the telemetry framework required to understand how agents operate in production. Instrument and analyze intent routing, agent and tool selection, context utilization, handoffs, latency, errors, completion rates, confidence, user interventions, and business outcomes. Build the feedback loops necessary to continuously improve agent performance
- Agent Evaluation Suites: Build robust evaluation frameworks for testing agent quality, reliability, routing, context utilization, tool execution, and end-to-end workflow completion. Establish both offline and production evaluation methodologies that enable measurable improvements over time
- Model Workbench Development: Lead the creation of a Model Workbench designed for marketers and other non-technical users, enabling them to safely leverage LLMs, traditional ML, agents, and workflows without requiring deep technical expertise
- MCP Capability and Tool Registry: Oversee the registration, documentation, governance, and discoverability of Model Context Protocol servers, tools, agents, and platform capabilities. Ensure capabilities are easy for both developers and agents to understand, select, and invoke correctly
- Cross-Functional Architecture and Organizational Alignment: Drive alignment across Product, Engineering, Data Science, Design, Analytics, Security, and business stakeholders around shared agentic architecture, context standards, ownership models, evaluation criteria, and platform priorities. Establish clear accountability and operating models for capabilities that span multiple teams
- Platform Standards and Governance: Define standards for how agents, tools, context sources, telemetry, and workflows are built and integrated across the organization. Balance local team autonomy with the consistency required to create a coherent platform experience
- Technical Troubleshooting and Prototyping: Actively participate in troubleshooting and debugging using tools such as LangSmith and related observability platforms. Lead by example by rapidly building proof-of-concepts to validate technical approaches, identify architectural constraints, and demonstrate new product opportunities
- Advocacy for Rapid Iteration: Promote a culture of rapid prototyping, experimentation, and evidence-based iteration. Use lightweight development and “vibe coding” where appropriate to quickly turn ideas into working experiences before investing in production-scale implementations
Skills
- Demonstrated experience leading complex technical products, particularly those involving LLMs, AI agents, workflow systems, developer platforms, ML infrastructure, or AI-driven applications
- Strong understanding of LLM agents, tool use, orchestration, multi-agent workflows, state management, context management, and the architectural patterns required to operate agentic systems reliably at scale
- Experience designing or working with context graphs, knowledge graphs, semantic systems, memory architectures, metadata platforms, or other systems that allow applications and models to understand relationships between users, data, actions, and business objects
- Strong understanding of instrumentation, telemetry, evaluation, and observability for complex software or AI systems
- Ability to define the signals required to distinguish between model failures, orchestration failures, context failures, tool failures, and UX failures
- Exceptional ability to align senior stakeholders and cross-functional teams around shared technical architecture, product priorities, ownership boundaries, and operating standards
- Comfortable leading initiatives where no single team controls the entire outcome
- Ability to reason across product experience, model behavior, data, infrastructure, APIs, organizational ownership, and operational processes rather than optimizing individual components in isolation
- Strong technical background with hands-on familiarity with tools such as LangSmith and experience working with APIs, workflow orchestration, LLM agent chaining, MCP, evaluation frameworks, and modern AI development environments
- Demonstrated ability to troubleshoot ambiguous technical problems, rapidly prototype potential solutions, and translate experimentation into scalable product and architectural decisions
- Excellent communication skills with the ability to translate highly technical concepts into clear product strategies, operating models, and decisions for technical and non-technical audiences
- Experience building or operating agentic infrastructure, AI platforms, context platforms, knowledge graphs, or developer ecosystems
- Experience integrating traditional machine learning with generative models and agentic systems , including using predictive models as tools or contextual inputs for agents
- Experience with workflow orchestration platforms and distributed systems involving multiple services, teams, and execution environments
- Experience designing AI telemetry, evaluation systems, experimentation frameworks, or production observability for LLM-powered products
- Demonstrated success establishing cross-functional technical standards and governance across multiple engineering and product organizations
- Experience building systems where context, telemetry, and evaluation form a continuous learning loop , allowing agent behavior and product experiences to improve based on real-world usage
Benefits
- Unlimited PTO
- Excellent medical, dental, and vision coverage
- Employee Equity
- Employee Discounts
- Virtual Wellness Classes
- Pet Insurance
Company Overview
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