City Of London, England
Job Summary
Agentic Consulting: Gemini Enterprise .
This role blends strategic business consulting, industry-specific domain expertise, and deep technical execution utilizing Google Cloud’s Gemini Enterprise Agent Platform (the advanced ecosystem unifying what was previously Vertex AI and enterprise agent tooling).
Position Overview
As an Agentic Consulting Specialist / Product Manager , you will bridge the gap between high-level business strategy and technical implementation. Your primary focus will be designing, building, and deploying autonomous AI Agents that automate complex, multi-step, multi-app workflows. You will serve as the trusted advisor to both C-suite business stakeholders and IT engineering teams, ensuring that Agentic AI solutions solve real-world industry pain points.
Key Responsibilities
1. AI Product Strategy and Vision (Consulting & Advisory)
You will drive the adoption of Agentic AI by demonstrating how autonomous agents can shift an organization from reactive software usage to proactive, automated process execution.
Domain-Specific Solutioning: Apply deep industry knowledge to pinpoint high-value automation use cases:
Financial Services (FS): Automating credit risk reporting, compliance checks, or fraud investigation workflows.
Retail & Consumer Packaged Goods (RCPG): Real-time supply chain adjustments, automated inventory routing, or predictive marketing campaign launches.
Life Sciences & Healthcare (LSH): Accelerating clinical trial documentation search, patient intake routing, or medical literature synthesis.
Manufacturing & Technology: Email-based order processing automation, predictive asset maintenance workflows, or software development lifecycle (SDLC) speed-ups.
Google Cloud Architecture Alignment: Consult clients on utilizing the Gemini Enterprise Agent Platform to replace traditional, rigid chatbots with dynamic, multi-agent frameworks.
Agile Discovery: Facilitate workshops with business and IT stakeholders to evaluate business processes, estimate ROI, and map out the vision for an "agentic taskforce."
2. Product Development and Execution (Product Management)
You will own the lifecycle of the AI agent from conceptualization to stable production deployment, managing cross-functional technical teams.
Roadmap & Backlog Management: Qualify agent use cases based on technical feasibility and business impact. Maintain the product backlog using Agile methodologies.
Technical & Functional Specifications: Translate complex business rules into concrete logic definitions. You will write specifications outlining:
Agent Logic and Reasoning: Defining paths using Agent Studio (for low-code/no-code workflows) or specifying requirements for the Agent Development Kit (ADK) (for developer code-first graph-based sub-agent networks).
Data Grounding: Defining how agents safely connect to enterprise data sources (e.g., BigQuery, Google Workspace, or third-party CRM/ERP systems) using secure connectors.
Integrations: Utilizing the Agent2Agent (A2A) protocol to ensure a Google agent can seamlessly hand off tasks to partner agents (e.g., Salesforce, Workday, ServiceNow).
Guardrails & Quality Assurance: Define functional requirements for safety and performance. Work with engineers to utilize Agent Simulation and Agent Evaluation tools to test agents against synthetic user profiles and prevent prompt injection vulnerabilities via Model Armor
Skill Requirements
3. Stakeholder Alignment and Go-to-Market (GTM)
An agent is only valuable if it is trusted and adopted. You will act as the ultimate product evangelist and governance lead.
Cross-Functional Orchestration: Serve as the central hub connecting Engineering, User Experience (UX), Sales, and Marketing teams to ensure a smooth product rollout.
Deployment and Enterprise Governance: Collaborate with enterprise IT admins to ensure agents are securely deployed into the organization's Gemini Enterprise app hub. Ensure every custom agent is registered under an Agent Identity and governed via the central Agent Registry .
Feedback Loops & Iteration: Gather direct user feedback from client teams, analyze Agent Observability traces to see how the agent reasons through its tasks, and continuously refine prompts, tools, and workflows to improve completion rates.
KPI Tracking: Monitor and present product performance metrics, such as:
Task Success Rate (percentage of workflows completed without human intervention).
Time-to-Resolution Reduction (e.g., reducing email processing times from hours to real-time).
API & Compute Cost Efficiency (tracking vCPU and token spend).
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