Product Manager
Product
Bengaluru, Karnataka, India
Posted on Sep 18, 2026
About SatSure
SatSure is a Series A+ startup that works at the intersection of space technology, remote sensing, and AI/Computer Vision, solving problems pertaining to financial inclusion of smallholder farmers, climate sustainability, and critical infrastructure asset monitoring. We are a team of 150+ dreamers who are looking to build a one-of-a-kind satellite company globally that pushes the boundaries of what data from space can do for people and businesses on Earth! There is no playbook yet for a company like ours on how to scale from 50 to 500 enterprise customers, but we are confident that we are building one that will be copied by many in the years to come.
Role
We're looking for a Product Manager to own the AI/ML Platform layer of SatSure's platform — one of three platform pillars, alongside App Platform (client-facing dashboards) and Data & Compute (spatial processing infrastructure). This role serves data scientists, ML engineers, and consuming product teams by owning the reusable systems and workflows used to build, govern, evaluate, deploy, and operate AI capabilities reliably at scale.
This is intentionally a technical product role. We're looking for someone with strong ML/DS or AI-platform depth and demonstrated product ownership across discovery, prioritisation, delivery and adoption; a formal PM title is not essential. The Product Lead owns customer problems, AI use cases and commercial outcomes, while this role owns how those capabilities are built, governed, deployed and operated. ML/DS leadership remains accountable for modelling decisions.
Responsibilities
- Defining the Bigger Picture - Research AI-platform architecture, MLOps, evaluation, governance, and developer tooling
- Identify where model/data improvements can unlock new platform capability or resolve existing quality gaps
- Develop and communicate a product vision for the AI/ML Platform pillar, in coordination with the App Platform, Data & Compute and AI Product Lead
- Manage a roadmap for model/data work that maps to platform-wide priorities
- Define MVP scope for reusable platform capabilities and build effective feedback loops with DS/ML engineering and consuming product teams
Steering Product Development
- Define platform requirements for data discovery, ownership, access, permitted use, quality, dataset versioning, annotation and end-to-end lineage
- Define and track platform metrics such as adoption, experiment-to-production time, reproducibility, reuse, reliability, monitoring coverage and cost; the AI Product Lead owns use-case and business outcomes
- Work with data scientists and ML engineers to define reusable evaluation frameworks, release workflows, evidence requirements and platform gates; ML/DS leadership owns modelling decisions
- Own model catalogue and registry workflows for reuse, versioning, promotion and retirement; product value and modelling approach remain with the AI Product Lead and ML/DS leadership respectively
- Define observability requirements across data quality, model behaviour and system health, including drift, degradation, failures and operational limits
- Manage the AI/ML platform backlog, prioritise shippable increments and keep adoption, reliability, governance and cost trade-offs transparent
Driving Platform Success
- Own the platform product lifecycle from experimentation and validation through deployment, monitoring, rollback, retraining, replacement and retirement; engineering owns implementation and operations
- Provide App Platform and the AI Product Lead with stable model-service interfaces, capability metadata, operational limits and reusable integration patterns
- Enable model documentation, intended-use and limitation records, evaluation evidence, approvals, audit trails and access controls with Security, Legal and governance owners
- Provide feasibility, scalability, reliability, cost and service-level inputs when platform capability affects delivery or customer commitments; the AI Product Lead leads the commercial commitment
Must have
- 3+ years of relevant experience across technical product management, ML engineering, data science, MLOps, AI platforms or data platforms, with strong understanding of the end-to-end ML lifecycle
- Strong first principles thinking and high agency
- Demonstrated ownership of a technical product or platform capability across discovery, prioritisation, cross-functional delivery, adoption, and measurable improvement
- Strong analytical skills and comfort using data to inform decisions
- Excellent written and verbal communication, stakeholder management, and ability to make clear trade-offs across technical and product teams
- Ability to work effectively with multiple stakeholders and handle ambiguity
Good to have
- Hands-on experience building, training, evaluating, or shipping machine-learning or computer-vision models; a formal PM title is not essential if product ownership is demonstrated
- Deep understanding of geospatial data, especially satellite Earth observation (EO) data
- Experience with internal developer platforms, experiment tracking, orchestration, registries, feature stores, CI/CD or continuous-training workflows
- Experience with data catalogues, lineage systems, annotation platforms, data-quality pipelines or model governance
Benefits:
- Medical Health Cover for you and your family, including unlimited online doctor consultations
- Access to mental health experts for you and your family
- Dedicated allowances for learning and skill development
- Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, bereavement leaves