New opportunity

Product Manager

SatSure · Bangalore, India

About this role

SatSure is seeking a Product Manager to own the AI/ML Platform layer, one of three core pillars supporting their space technology and remote sensing operations. This technical product role focuses on building, governing, evaluating, deploying, and operating AI capabilities at scale for data scientists and ML engineers. Responsibilities include defining the platform vision, managing the roadmap for model and data workflows, and establishing reusable evaluation frameworks and release processes. The role requires defining metrics for platform success, such as experiment-to-production time, reproducibility, and reliability, while maintaining transparency regarding cost and governance trade-offs. The Product Manager will own the platform lifecycle from experimentation through to retirement, ensuring stable model-service interfaces and robust observability for data quality and model health. Requirements include at least 3 years of experience in technical product management, ML engineering, data science, or MLOps, with a strong grasp of the end-to-end ML lifecycle. Candidates should demonstrate high agency, strong analytical skills, and the ability to manage cross-functional stakeholders. Preferred qualifications include hands-on experience with ML or computer vision models, familiarity with geospatial/satellite data, and experience with developer platforms, CI/CD, and model governance systems. Benefits include comprehensive medical coverage, mental health support, and learning allowances.

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

Skills for this role

Technical Product ManagementML EngineeringData ScienceMLOpsAI PlatformsData PlatformsMachine Learning LifecycleProduct OwnershipStakeholder ManagementComputer VisionGeospatial DataSatellite Earth ObservationInternal Developer PlatformsExperiment TrackingOrchestrationModel RegistriesFeature StoresCI/CDContinuous TrainingData CataloguesData LineageAnnotation PlatformsData Quality PipelinesModel Governance

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

Standardizing the ML Lifecycle
The role requires building and governing reusable frameworks for model experimentation, deployment, and retirement. You must prioritize creating consistent release workflows and evaluation gates that allow data scientists to move from experimentation to production reliably without sacrificing model quality or reproducibility.
Platform Observability and Governance
You are responsible for defining requirements for data quality, model health, and system monitoring. This involves implementing robust observability for drift and degradation while ensuring that model-service interfaces remain stable for downstream teams, such as the App Platform and AI Product Lead.
Balancing Technical Trade-offs
A core duty is managing the backlog while maintaining transparency regarding cost, reliability, and governance trade-offs. You must effectively communicate these technical constraints to cross-functional stakeholders to ensure that platform capabilities align with broader business commitments.

Likely challenges to think through

Navigating Internal Stakeholder Dependencies
You must serve multiple internal teams (DS, ML Engineering, App Platform) without having direct authority over their modelling decisions. Prepare by demonstrating how you build consensus and influence technical teams through data-driven insights rather than hierarchical command.
Managing Ambiguity in a Scaling Startup
SatSure is scaling rapidly without a set playbook for their specific space-tech niche. Prepare to discuss how you define MVP scopes for platform capabilities when requirements are evolving and how you maintain progress when faced with high levels of operational uncertainty.

Practice your answers

Technical practice

Designing an MLOps Platform Roadmap

What to coverExplain how you would prioritize features for an internal AI/ML platform when balancing the needs of data scientists, ML engineers, and downstream product teams. Discuss how you would measure success using metrics like experiment-to-production time and model reproducibility.

Model Governance and Lifecycle Management

What to coverDescribe your approach to managing the end-to-end lifecycle of a model, from initial experimentation to retirement. Focus on how you would implement model registries, versioning, and audit trails to ensure transparency and compliance.

Defining Observability for ML Systems

What to coverWhat are the critical components of an observability framework for production AI models? Address how you would monitor for data quality, model drift, and system health while providing actionable insights to engineering teams.

Trade-offs in Platform Architecture

What to coverWhen building reusable AI platform components, how do you balance the need for flexibility for data scientists against the need for standardization and reliability? Discuss the trade-offs between custom-built tools and off-the-shelf MLOps solutions.

Data Lineage and Quality Pipelines

What to coverExplain the importance of data lineage and quality pipelines in a satellite imagery context. How would you define requirements for data discovery, versioning, and annotation to ensure reliable model training?

Managing Model-Service Interfaces

What to coverHow do you ensure stable model-service interfaces for consuming product teams? Discuss the role of capability metadata and operational limits in preventing downstream failures when models are updated or replaced.

Evaluation Frameworks for AI

What to coverHow would you design a reusable evaluation framework that works across different AI use cases? Address how you would define 'platform gates' to ensure models meet quality standards before deployment.

Handling Geospatial Data Constraints

What to coverGiven the nature of satellite Earth observation data, what specific challenges would you anticipate in building a data platform? Suggest how you would address storage, processing, or annotation bottlenecks.

Behavioural practice

Managing Conflicting Stakeholder Priorities

What to coverDescribe a situation where you had to balance the competing needs of technical teams (e.g., ML engineers) and product stakeholders. How did you communicate the trade-offs and reach a consensus?

Driving Adoption of Internal Tools

What to coverProvide an example of how you successfully drove the adoption of a new technical platform or tool. What strategies did you use to overcome resistance and ensure the tool met the users' actual needs?

Navigating Ambiguity in Technical Strategy

What to coverTell me about a time you had to define a product vision in an ambiguous environment. How did you gather the necessary information to set a clear direction and gain buy-in from leadership?

Learning from a Failed Initiative

What to coverDescribe a technical project or platform feature that did not achieve its intended outcome. What did you learn from the process, and how did you adjust your approach to future product development?

Sharpen your knowledge

Topics to prepare

End-to-End MLOps Lifecycle
Review the stages of the ML lifecycle from experimentation to retirement. Exercise: Map out a hypothetical workflow for a satellite imagery model, identifying specific platform gates for model versioning, evaluation, and deployment. This is relevant because the role requires owning the platform lifecycle and defining reusable release processes.
AI Platform Governance and Observability
Study frameworks for model governance, including audit trails, data lineage, and model health monitoring. Exercise: Draft a list of key performance indicators (KPIs) for monitoring model drift and data quality in a production environment. This is relevant as the role involves defining observability requirements and maintaining transparency regarding governance trade-offs.

Skills to revise

Technical Roadmap Prioritization
Practice prioritizing a backlog based on platform-wide metrics like experiment-to-production time and reliability. Exercise: Create a prioritization matrix for three competing platform features (e.g., automated retraining, data lineage tracking, and model registry improvements) based on impact and effort. This is relevant to the requirement of managing the AI/ML platform roadmap.
Cross-functional Stakeholder Management
Develop strategies for communicating technical trade-offs to non-technical or semi-technical stakeholders like Legal or Product Leads. Exercise: Write a brief proposal explaining the cost-benefit trade-offs of implementing a new model evaluation framework to a hypothetical AI Product Lead. This is relevant because the role requires balancing technical requirements with commercial commitments.
Geospatial Data Familiarity
Gain a conceptual understanding of satellite Earth observation data challenges, such as resolution, spectral bands, and temporal frequency. Exercise: Research common data quality issues in satellite imagery and how they impact model training pipelines. This is relevant as a preferred qualification for the role at SatSure.

Your preparation checklist

Map the MLOps Lifecycle to Platform Pillars
Create a visual representation or document outlining how you would structure an AI/ML platform to support SatSure's specific needs, such as geospatial data processing and computer vision. Highlight how you would manage the transition from experimentation to production, including model registry, versioning, and deployment workflows.
Develop a Platform Metrics Framework
Draft a list of key performance indicators (KPIs) you would use to measure the success of an AI platform. Focus on metrics mentioned in the job description, such as experiment-to-production time, model reproducibility, and system reliability, and explain how you would track these to inform roadmap prioritization.
Prepare a Case Study on Technical Trade-offs
Prepare a brief walkthrough of a past project where you had to balance technical debt, cost, and governance requirements against the need for rapid feature delivery. Emphasize how you communicated these trade-offs to cross-functional stakeholders like data scientists and engineering leads.
Formulate Strategic Questions for the Interview
Prepare thoughtful questions regarding SatSure's current AI platform maturity, such as: How do you currently handle the feedback loop between the App Platform and the AI/ML Platform? What are the biggest bottlenecks in your current model deployment pipeline? How does the team currently balance the need for model governance with the speed of experimentation?

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…