Principal Product Manager

Job Title : Principal Product Manager

Location : San Francisco, CA 94133 (Hybrid)

Duration : 12 months

Must Have Skills:

Product Management

B2B Experience

Detailed Job Description:

Principal Product Manager (AI)

Design and Deploy Agentic AI Systems

Architect multi-step, tool-using agent workflows across WSI s AI platform ecosystem (ChatGPT Enterprise, Claude, Gemini, Salesforce Agentforce)

Design orchestration layers, tool/MCP integrations, and human-in-the-loop checkpoints for agentic commerce and operational workflows

Evaluate when agentic approaches are warranted versus simpler prompt-based or rule-based solutions and hold that line

Establish reliability, evaluation, and failure-handling standards for production agentic systems

Redesign Workflows for AI-Native Operation

Partner with brand and functional teams to map current-state workflows and identify meaningful AI transformation opportunities

Apply structured decision-making to determine the right solution: agentic AI, prompt-based automation, traditional RPA, or process redesign

Co-design workflows that are scalable, auditable, and owned by the business not dependent on the AI Strategy team to operate

Apply Cross-Platform AI Expertise

Operate fluently across WSI s deployed AI platforms: ChatGPT Enterprise (OpenAI), Claude (Anthropic), Gemini (Google), and Salesforce Agentforce

Evaluate platform strengths and limitations per use case reasoning depth, context handling, tool use, latency, cost, and governance constraints

Contribute to platform vendor relationships as a practitioner peer, not just a buyer: push back, co-develop, and represent enterprise requirements

Stay current on MCP (Model Context Protocol), emerging agent frameworks, and evolving platform capabilities

Enable Teams to Own Their AI Capabilities

Co-build solutions with business stakeholders; never build in isolation

Develop playbooks, templates, and repeatable patterns teams can evolve independently

Establish clear ownership and transition models; avoid creating dependency by design

Measure success by adoption, independence, and sustained operation not by throughput of solutions delivered

Build Selectively and Ship to Production

Develop lightweight automations, agent workflows, and prompt systems where required to unblock progress

Ensure every solution has a defined path to production, clear ownership, monitoring, and maintenance

Balance accuracy, cost, latency, reliability, and auditability in every design decision

Drive Scalable AI Adoption and Governance

Define and evolve enterprise AI tooling standards, access controls, and data governance practices

Partner with Engineering, Security, Legal, and Finance on AI governance frameworks and risk management

Support client s enterprise AI skills program: lead workshops, develop training, and build AI fluency across functional teams