Job Description
## Machine Learning Product Manager
**Department:** AI Product Management | **Industry:** Machine Learning
**Location:** Chandigarh | **Job Type:** Full-time | **CTC:** INR 27.0-45.7 LPA
### Role Overview
The organisation is seeking a qualified Machine Learning Product Manager to support ongoing initiatives in AI Product Management. The role is positioned at the mid level and reports into the broader AI Product Management charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to ai product management.
- Apply working knowledge of Statistical Modelling and TensorFlow/PyTorch to daily execution.
- Coordinate with cross-functional stakeholders within Machine Learning.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Sc (Data Science) or equivalent qualification.
- 6 years of relevant experience (mid level).
- Working proficiency in: Statistical Modelling, TensorFlow/PyTorch, Feature Engineering, Computer Vision/NLP Basics, Deep Learning, Python, Data Preprocessing, AI Product Management.
### Additional Notes
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The next logical step from this position is a senior or lead-track role within the same function. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. References may be requested closer to the offer stage rather than at the start of the process. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
**Department:** AI Product Management | **Industry:** Machine Learning
**Location:** Chandigarh | **Job Type:** Full-time | **CTC:** INR 27.0-45.7 LPA
### Role Overview
The organisation is seeking a qualified Machine Learning Product Manager to support ongoing initiatives in AI Product Management. The role is positioned at the mid level and reports into the broader AI Product Management charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to ai product management.
- Apply working knowledge of Statistical Modelling and TensorFlow/PyTorch to daily execution.
- Coordinate with cross-functional stakeholders within Machine Learning.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Sc (Data Science) or equivalent qualification.
- 6 years of relevant experience (mid level).
- Working proficiency in: Statistical Modelling, TensorFlow/PyTorch, Feature Engineering, Computer Vision/NLP Basics, Deep Learning, Python, Data Preprocessing, AI Product Management.
### Additional Notes
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The next logical step from this position is a senior or lead-track role within the same function. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. References may be requested closer to the offer stage rather than at the start of the process. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.