Job Title: Product Manager with AI
Work Location & Reporting Address: San Francisco, CA 94133 (Onsite-Hybrid)
Contract duration: 6 Months
Must Have Skills:
Product Management
B2B Experience
Nice to Have Skills:
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
Never miss an opportunity! Create an alert based on the job you applied for.
