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Open nowPosted 117 days ago

AI Product Lead

SatSure Analytics India29 open roles

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Your applicationOpen nowAI Product LeadSatSure Analytics India

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  2. CV RocketCV written for this posting.

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. SatSure Analytics India postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 117 days ago

SatSure Analytics India median: 1 days open

The posting

About SatSure

SatSure builds decision intelligence systems using artificial intelligence, large-scale data platforms, and complex real-world datasets.

We are working on Dhaarini, an internal AI initiative focused on creating reusable capabilities from multimodal and temporal data.

Dhaarini is not a single model or a standalone application. It is intended to support capabilities such as reusable representations, multimodal retrieval, temporal understanding and AI services that can be used across SatSure’s platform and products.

Role

We are looking for an AI Product Lead to help structure and drive Dhaarini’s AI capability development.

You will work directly with the CTO and collaborate with AI scientists, ML engineers, platform teams and product managers.

The role is suited to someone who understands AI development well enough to frame the right problems, define clear success criteria and help move useful experiments toward reusable product capabilities.

You are not expected to be the deepest researcher or system architect. You will, however, need to be comfortable working with technical teams, asking difficult questions, and bringing product clarity to an area where the answers may not always be obvious.

This is an individual-contributor role with cross-functional responsibility.

Responsibilities:

Shape the AI capability roadmap

  • Work with the CTO and AI leaders to define and prioritise Dhaarini’s AI capabilities.
  • Translate broad technical ideas into clear problem statements and capability requirements.
  • Balance longer-term capability development with near-term validation.
  • Support build, adapt or use-existing decisions based on evidence and feasibility.

Structure experimentation and evaluation

  • Convert ideas into clear hypotheses, experiments and decision points.
  • Work with technical teams to define baselines, evaluation criteria and success measures.
  • Ensure experiments answer meaningful technical or product questions.
  • Capture learnings and support decisions on whether work should progress, change direction or stop.

Support capability productisation

  • Identify when a capability is ready to move beyond experimentation.
  • Define its intended usage and product requirements.
  • Work with platform and engineering teams to move validated capabilities toward integration.
  • Reduce the number of useful experiments that remain limited to notebooks, demos or isolated implementations.

Drive cross-functional execution

  • Keep AI, platform and product teams aligned on shared priorities.
  • Identify dependencies, risks and unresolved decisions early.
  • Run reviews focused on evidence, learnings and next steps.
  • Escalate important trade-offs with clear context and recommendations.

What success looks like

Within the first 6–12 months, you should have:

  • Established a clear Dhaarini AI capability roadmap.
  • Introduced a consistent way to frame and evaluate experiments.
  • Helped validate two or more important technical hypotheses.
  • Moved at least one reusable capability toward platform integration.
  • Created a clear adoption path for at least one capability.
  • Reduced repetitive or disconnected experimentation across teams.

Experience:

  • 5–7 years of experience across AI products, ML products, data products, technical product management, or applied AI.

Must have:

  • Experience working directly with AI/ML, data science or engineering teams.
  • At least one example of taking an AI or data capability from problem definition toward production or operational use.
  • Practical understanding of the AI lifecycle, including data, experimentation, evaluation, deployment, and monitoring.
  • Ability to turn ambiguous technical ideas into clear priorities, requirements, and decisions.
  • Strong product judgement and cross-functional execution skills.
  • Ability to understand technical discussions and challenge assumptions constructively.
  • Strong written and verbal communication.
  • High ownership and comfort working in an evolving, zero-to-one environment.

We are looking for someone who has worked directly with the AI product lifecycle, not only integrated third-party AI services into conventional software products.

Good to have:

  • Experience with computer vision, multimodal AI, embeddings or retrieval systems.
  • Experience with MLOps, AI platforms or large-scale data systems.
  • Experience working with multimodal or time-series data.
  • Experience in a startup, deep-tech or early-stage product environment.

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, and bereavement leaves
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