(Agentic) Product Manager
Backbase Hyderabad, Telangana, India
Financial Services · 1,001-5,000 employees
About the role
You will lead the development of autonomous AI agents for a banking platform, defining the product roadmap and ensuring enterprise-grade quality. You will collaborate across multiple product pillars to integrate AI solutions and drive cross-functional alignment.
What they look for
Requirements
The ideal candidate has experience shipping AI-native or enterprise software products with deep LLM integration. You must possess strong systems thinking, technical depth in AI/LLM capabilities, and a track record of managing complex, cross-functional initiatives.
Full description
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
Execution leadership
- 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
Partnership with platform PMs
- 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
We're Looking For
Experience shipping one or more of the following:
AI-native products or features that users love · 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.
Core capabilities
- 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.
Nice to have
- 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
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