As a Product Manager at Korza, you will lead the product strategy, roadmap, and positioning for the company's ML platform, enterprise AI products, and AI control plane. You will bridge the gap between consulting and product development by translating complex enterprise business challenges into scalable, multi-tenant platform features. Responsibilities include architecting AI-first user experiences, defining PRDs, and collaborating with cross-functional teams including AI/ML engineers and designers. You will also own product performance metrics such as adoption, latency, and task completion accuracy. Requirements: 5 years of product management experience in enterprise software, SaaS, or data platforms. You must possess a strong understanding of modern software architecture, APIs, and enterprise SDLC. Direct experience or functional expertise with LLMs, RAG frameworks, prompt systems, or agent-based workflows is required. A proven track record of taking products from 0-to-1 and managing complex, multi-stakeholder requirements is essential. You will be a pivotal member of the core hub in Bangalore, working in a high-growth environment to shape the future of AI-driven software development for global Fortune 100-1000 clients.
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A focused preparation guide, built around this job’s responsibilities and requirements.
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01
Understand the role
What matters most
Translating Consulting Insights into Scalable Features
A core requirement is bridging the gap between bespoke enterprise client challenges and a multi-tenant platform. You must demonstrate the ability to identify patterns in client-specific requests and abstract them into reusable, scalable product features that benefit the broader platform roadmap.
Architecting AI-First User Experiences
You are expected to define PRDs for complex AI workflows, including RAG, LLMs, and agentic systems. This requires translating technical AI capabilities into intuitive, functional user journeys that solve specific enterprise business problems while maintaining high performance standards.
Ownership of Technical Performance Metrics
Beyond standard adoption metrics, you must own technical KPIs such as latency and task completion accuracy. This requires a deep understanding of the underlying AI/ML infrastructure to iterate on product utility based on quantitative performance data.
Likely challenges to think through
Balancing Customization with Platform Scalability
Working with Fortune 100-1000 clients often leads to pressure for custom features. The challenge is to maintain a unified, scalable product vision while satisfying high-stakes enterprise requirements. Prepare by articulating a framework for evaluating when to build custom solutions versus when to prioritize platform-wide core features.
Managing Ambiguity in Emerging AI Workflows
Because the role involves defining the future of AI-driven SDLC, you will face high levels of ambiguity regarding how users interact with agentic systems. Prepare by demonstrating your process for rapid prototyping, user testing, and iterating on product requirements in a fast-paced, high-growth environment.
03
Practice your answers
Technical practice
Translating Enterprise Challenges into Scalable Features
What to coverExplain your process for identifying common patterns across disparate enterprise client needs. Focus on how you abstract specific consulting requests into reusable, multi-tenant platform features while maintaining flexibility for individual client requirements.
Designing AI-First User Experiences
What to coverDescribe how you approach creating PRDs for LLM-based products where output is non-deterministic. Discuss how you balance user control with automation and how you define success for agentic workflows.
Managing RAG and Prompt System Performance
What to coverDiscuss the trade-offs between latency, cost, and accuracy when implementing RAG frameworks. Suggestion: Prepare to explain how you monitor and iterate on prompt systems to improve task completion accuracy over time.
Defining Metrics for AI Platforms
What to coverBeyond standard SaaS metrics like retention, how do you measure the effectiveness of an AI control plane? Focus on technical performance indicators like latency and accuracy, and how these correlate with business value for Fortune 1000 clients.
Navigating Enterprise SDLC and Architecture
What to coverHow do you ensure your product roadmap aligns with modern backend infrastructure and API standards? Discuss your approach to collaborating with engineering teams to ensure scalability and security in an enterprise environment.
0-to-1 Product Development
What to coverDescribe your framework for validating a new AI product concept before full-scale development. Focus on how you manage risk and stakeholder expectations during the initial launch phase.
Behavioural practice
Managing Complex Multi-Stakeholder Requirements
What to coverProvide an example of a time you had to reconcile conflicting requirements from multiple enterprise stakeholders. Describe the situation, the specific actions you took to prioritize features, and the final outcome for the product roadmap.
Handling Ambiguity in High-Growth Environments
What to coverDescribe a situation where you were tasked with solving a highly ambiguous problem with limited data. Explain how you brought structure to the project and how you communicated your progress to the team.
Cross-Functional Collaboration with Engineering
What to coverShare an example of a disagreement you had with an AI/ML engineer regarding product feasibility or technical direction. Detail how you resolved the conflict while maintaining a positive working relationship and meeting project goals.
Adapting to Rapidly Changing AI Technology
What to coverDescribe a time when a shift in AI capabilities or market trends required you to pivot your product strategy. Explain your decision-making process and how you managed the transition for your team and stakeholders.
02
Sharpen your knowledge
Topics to prepare
AI-First Product Strategy
Review how to translate complex LLM, RAG, and agentic workflows into scalable, multi-tenant platform features. Exercise: Draft a strategy document for an AI control plane that balances user experience with technical latency requirements, ensuring it addresses enterprise-grade security and scalability needs.
Enterprise SDLC and Architecture
Deepen understanding of modern software architecture, APIs, and the enterprise software development lifecycle. Exercise: Map out how a new AI feature would integrate into an existing enterprise backend infrastructure, identifying potential bottlenecks in API design and data flow.
Product Metrics for AI Systems
Focus on defining and tracking performance metrics specific to AI products, such as latency, task completion accuracy, and adoption. Exercise: Create a dashboard framework for an AI agent product that tracks both technical performance (latency) and business value (task completion accuracy).
Skills to revise
PRD Writing for AI Workflows
Practice writing Product Requirement Documents (PRDs) that clearly define functional end-to-end user journeys for AI-driven products. Exercise: Write a PRD for a RAG-based enterprise tool, explicitly defining the input/output requirements, error handling for hallucinations, and user feedback loops.
0-to-1 Product Execution
Develop the ability to bring structure to ambiguous enterprise challenges. Exercise: Simulate a 0-to-1 project kickoff where you must prioritize features for a new AI platform based on conflicting requirements from multiple enterprise stakeholders.
04
Your preparation checklist
Prepare a 0-to-1 product case study
Draft a concise narrative of a product you launched from scratch. Focus on how you identified a market need, managed multi-stakeholder requirements, and navigated the enterprise SDLC to deliver a scalable solution.
Map your AI/ML technical experience
Create a summary of your hands-on experience with LLMs, RAG frameworks, or agentic workflows. Be prepared to explain how you translated these complex technical capabilities into user-facing features or business value.
Develop a framework for AI product metrics
Outline how you would measure success for an enterprise AI platform. Include specific KPIs such as latency, task completion accuracy, and adoption rates, and explain how you would use these to iterate on the product roadmap.
Draft technical questions for the engineering team
Prepare thoughtful questions regarding Korza's current AI control plane architecture, how they handle multi-tenancy in enterprise environments, and their approach to balancing rapid feature delivery with system stability.
Refresh your knowledge on API design, backend infrastructure, and enterprise-grade security requirements. Be ready to discuss how these factors influence your PRD writing and collaboration with AI/ML engineers.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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