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Product Manager - AI Pod

CASE Platforms · Bengaluru, India

About this role

About the role

We are looking for a Product Manager to join our AI pod and help build the future of investing in India. The AI Pod owns and drives product strategy to leverage AI-native features to improve existing product flows and ideate, build, and launch new AI-native products in the wealth-tech domain. This role offers a unique diversity of challenges, allowing you to work on upcoming products in their 0-1 journey while simultaneously embedding AI solutions in established products from 1-10 to 10-1000 and beyond. About CASE Platforms: CASE Platforms (formerly smallcase) is building the compounding layer for long-term wealth, serving 20+ million investors across consumer brands like smallcase, Tickertape, Zerodha CASE Funds, and creditcase. What You’ll Be Doing: Own the AI product roadmap and strategy, aligning vision directly with the leadership team. Evaluate and implement Generative AI, LLMs, AI agents, and automation to create genuine product value. Identify high-value use cases for AI solutions to improve product and business metrics. Translate consumer problems into clear product requirements, workflows, and measurable outcomes. Build, test, and iterate rapid MVPs and prototypes. Work closely with Engineering, Design, and Business teams to drive end-to-end execution. Define product metrics and measure adoption, engagement, retention, and business impact. Own product prioritization and make trade-offs between speed, impact, and complexity. Requirements: 3+ years of demonstrated product management experience, including the full product development lifecycle. Strong product sensibility with expertise in Information Architecture, UI/UX, and Interaction Design. Prior experience in building and scaling AI-native features or products with proven user adoption. In-depth knowledge of AI technology, including integrating AI providers, setting up evals, tool-call architecture, RAG pipelines, observability, orchestration, memory, prompt management, and hallucination mitigation. Proven ability to leverage data and analytical skills for product decisions. Exceptional communication and stakeholder management skills. Preferred: Strong understanding of financial and stock market terms. Workplace: This role is based in our Bangalore office and requires 3 days weekly in the office, with flexibility for 2 remote days each week.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product ManagementGenerative AILLMsAI AgentsRAGPrompt EngineeringProduct StrategyUI/UXInformation ArchitectureData AnalysisMVP DevelopmentAI ObservabilityAI Orchestration

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

AI-Native Product Strategy and Roadmap
You must define and own the AI product vision, aligning it with leadership. This involves identifying high-value use cases for Generative AI, LLMs, and agents that directly improve business metrics and user engagement across existing platforms like smallcase and Tickertape.
Technical Execution and AI Lifecycle Management
You are responsible for the end-to-end development of AI features. This requires deep technical fluency in RAG pipelines, tool-call architecture, prompt management, and observability to ensure high-quality, reliable outputs while mitigating hallucinations.
Rapid Prototyping and Iteration
The role demands the ability to build, test, and iterate on MVPs for 0-1 products. You must translate complex consumer problems into clear requirements and workflows, ensuring that prototypes are validated quickly before scaling.
Cross-Functional Stakeholder Alignment
You must coordinate effectively with Engineering, Design, and Business teams. Success depends on your ability to translate technical specifications into high-quality user experiences and manage trade-offs between development speed, product impact, and technical complexity.

Likely challenges to think through

Balancing 0-1 Innovation with 1-1000 Scaling
You will simultaneously manage new product incubation and the integration of AI into established, high-scale products. Prepare to discuss how you prioritize resources differently when building from scratch versus optimizing existing, high-traffic workflows.
Managing AI Reliability in Wealth-Tech
In the financial domain, accuracy is critical. You will face the challenge of mitigating hallucinations and ensuring AI outputs are trustworthy. Prepare to explain your approach to setting up rigorous evaluation frameworks and grounding techniques for LLMs.
Translating Technical AI Capabilities into Business Value
There is a risk of focusing on AI for its own sake. You must demonstrate how to filter technical possibilities through the lens of business metrics like retention and adoption. Prepare to discuss how you measure the 'genuine product value' of an AI feature.

Practice your answers

Technical practice

Designing RAG Pipelines for Financial Data

What to coverExplain how you would architect a Retrieval-Augmented Generation pipeline to ensure accuracy when answering user queries about wealth-tech products. Focus on how you would handle data retrieval, context window management, and grounding techniques to minimize hallucinations.

Evaluating AI Model Performance

What to coverDescribe your approach to setting up evaluations for AI-native features. Discuss the metrics you would use to measure the quality of LLM outputs and how you would balance automated testing with human-in-the-loop validation.

Managing AI Tool-Call Architecture

What to coverHow do you design the interaction between an LLM and external tools or APIs? Discuss the trade-offs between latency and accuracy when implementing agentic workflows that require multiple tool calls.

Observability in AI Products

What to coverWhat strategies do you use to monitor AI-native features in production? Detail how you track prompt performance, error rates, and user feedback to iterate on the product effectively.

Balancing Speed, Impact, and Complexity

What to coverWhen prioritizing features for a 0-1 product versus scaling an existing one, how do you make trade-offs? Provide a framework for deciding when to build a custom AI solution versus integrating an existing provider.

Information Architecture for AI Interfaces

What to coverHow does the integration of AI agents change your approach to UI/UX and information architecture? Discuss how you ensure users understand the capabilities and limitations of an AI-driven interface.

Prompt Management and Versioning

What to coverExplain your process for managing and versioning prompts in a production environment. How do you ensure consistency in model behavior as you scale features across different user segments?

Data-Driven Product Decisions

What to coverDescribe a scenario where you used data to identify a high-value AI use case. Explain how you translated that insight into measurable product outcomes and tracked the impact on engagement or retention.

Behavioural practice

Aligning AI Strategy with Leadership

What to coverDescribe a time you had to pitch a complex product strategy to leadership. How did you ensure your vision for AI-native features aligned with broader business goals and secured the necessary buy-in?

Cross-Functional Collaboration

What to coverGive an example of how you worked with Engineering and Design teams to execute a challenging AI project. How did you manage technical constraints while maintaining a high-quality user experience?

Handling Product Failure or Iteration

What to coverTell me about a time an AI feature or MVP did not meet the expected adoption metrics. What was your process for diagnosing the issue, and how did you pivot or iterate based on those findings?

Managing Stakeholder Expectations

What to coverDescribe a situation where you had to manage conflicting priorities between different internal teams. How did you communicate your roadmap decisions and maintain alignment while driving the project forward?

Sharpen your knowledge

Topics to prepare

AI-Native Product Lifecycle
Review the transition from 0-1 (new product ideation) to 1-10 and 10-1000 (scaling and embedding AI). Exercise: Map a hypothetical wealth-tech feature through these stages, identifying specific metrics for success at each phase to demonstrate strategic thinking.
AI Technical Architecture and Reliability
Deepen understanding of RAG pipelines, tool-call architecture, and hallucination mitigation. Exercise: Create a conceptual framework for evaluating an LLM-based financial assistant, focusing on how to set up automated evals and observability to ensure accuracy and user trust.

Skills to revise

AI Orchestration and Prompt Management
Hands-on practice with managing prompts and orchestrating AI workflows. Exercise: Design a prompt chain for a complex financial query that requires tool-calling, and document how you would manage versioning and testing for that specific prompt.
Information Architecture for AI Interfaces
Apply UI/UX principles to AI-native features. Exercise: Sketch a user flow for an AI-driven investment insight tool, focusing on how to present AI-generated data clearly to minimize cognitive load and ensure the user remains in control of financial decisions.
Data-Driven Prioritization
Practice making trade-offs between speed, impact, and complexity. Exercise: Using a sample product backlog, apply a prioritization framework (like RICE) specifically to AI features, justifying why certain high-complexity features might be deferred in favor of rapid MVP iteration.

Your preparation checklist

Prepare a case study on AI-native product scaling
Create a concise walkthrough of a product you managed from 0-1 or 1-10. Highlight how you integrated AI (e.g., RAG, LLMs) to solve a specific user problem, the metrics you tracked, and how you mitigated hallucinations or latency issues.
Demonstrate technical fluency in AI architecture
Be prepared to discuss your experience with AI orchestration, tool-call architectures, and observability. Prepare examples of how you set up evaluation frameworks to measure the quality of AI outputs in production environments.
Map AI solutions to wealth-tech business metrics
Research the CASE Platforms ecosystem (smallcase, Tickertape, etc.). Develop a proposal for an AI-native feature that could improve engagement or retention for these specific platforms, focusing on how you would measure its business impact.
Refine your product prioritization framework
Prepare to explain your methodology for making trade-offs between speed, impact, and technical complexity. Have a concrete example of a time you had to pivot or deprioritize a feature based on data-driven insights.
Formulate strategic questions for leadership
Prepare thoughtful questions regarding the AI Pod's current roadmap, such as: 'How does the team balance building proprietary AI models versus integrating third-party providers?' or 'What are the biggest challenges in maintaining data privacy while scaling AI features across your financial products?'

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