Agentic Product Manager

About This Role

We are building an agentic banking platform for large enterprise banks. Multi-modal AI converts business intent into autonomous agents that orchestrate processes, integrate data, and make intelligent decisions — turning weeks of manual work into hours.

You will lead AI agents and intelligent automation — building capabilities that deploy autonomous agents to solve complex banking problems at enterprise scale. This is not a pillar-specific role. You'll work across three interconnected pillars (Durable Processes, Data Connectors, AI Agents), collaborating with peer PMs to architect and ship agentic solutions end-to-end.

You'll report directly to the Product Director and own the complete journey: from defining what agentic capabilities matter most, to shipping them, to measuring their impact. This role is for a PM who understands how AI agents fundamentally change enterprise automation, who can navigate technical and business complexity, and who ships with both ambition and rigor.

What You'll Own

Strategic ownership

• Define the AI capability roadmap — which features move the needle, which delight customers, which drive competitive advantage
• Make the hard calls: what ships in Year 1, what comes after, what's out of scope
• Own the quality bar — "enterprise grade" means reliable, explainable, compliant, and safe. Set and enforce that standard.
• Shape how AI surfaces across the platform — consistency in UX, tone, and behavior across copilot, solution builder, and other capabilities
  
  
  

• Lead cross-pillar alignment — work with peer PMs to define integration points and success metrics
• Conduct customer discovery: what AI capabilities matter most, what fears do they have about AI, what use cases are most valuable
• Make data-driven decisions: usage patterns, LLM cost, inference latency, customer sentiment all inform roadmap
• Unblock ambiguity: when technical feasibility isn't clear (cost, latency, quality), you synthesize information and move forward decisively
  
  
  

• Collaborate with peer PMs who own the platform pillars
• Establish clear integration contracts: where AI surfaces in their UIs, how data flows from their features to your LLM calls, how they measure success
  
  
  

• Enterprise software with AI capabilities built in (not tacked on)
• Products that required deep LLM integration and cost optimization
• Cross-functional initiatives that span multiple teams or product areas
• Experiences that balance power with usability.
  
  
  

• AI fluency, not just enthusiasm – You understand LLM capabilities and limits. You know the difference between a prototype and a production system. You think about cost, latency, hallucination, and compliance from day one.
• Systems thinking across pillars – You see how an AI feature in one pillar impacts the others. You optimize for platform coherence, not local feature wins.
• Customer reality grounding – You spend time with users and understand their AI skepticism. You don't oversell; you deliver reliably.
• Quality obsession – You know that "enterprise grade" is non-negotiable. Explainability, compliance, reliability, and safety are as important as feature velocity.
• Technical depth without being an engineer – You read research, understand prompt engineering tradeoffs, can discuss model selection and fine-tuning. You think rigorously about technical problems.
  
  
  

• Shipped copilot or AI-assisted features in a production SaaS product
• Experience with LLM cost optimization, latency budgeting, or inference infrastructure
• Familiarity with enterprise workflow, process automation, or BPM domains
• Track record of shipping cross-functional initiatives with competing stakeholders