Job Description
**AI Product Management Director** | Lucknow | Hybrid | INR 50.9-114.4 LPA
**About the role:**
- Position within AI Product Management, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for AI Product Management
**What you'll do:**
- Handle core ai product management activities on a day-to-day basis
- Use Python regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Python
- Model Evaluation & Tuning
- Data Preprocessing
- Feature Engineering
- Statistical Modelling
- AI Product Management
- Computer Vision/NLP Basics
- TensorFlow/PyTorch
- Machine Learning Algorithms
- M.Tech (Machine Learning)
- 8+ years of relevant experience
**Good to know:**
- There is a defined internal mobility path for people who want to specialise deeper or move into people management. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- The next logical step from this position is a senior or lead-track role within the same function. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Internal job postings are open to employees who've completed a minimum tenure in their current role.
- References may be requested closer to the offer stage rather than at the start of the process. Interested candidates should be prepared to walk through relevant past work during the interview process. High performers in this role typically move into a broader ownership scope within 18-24 months.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
- Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
- Compensation includes standard statutory benefits along with performance-linked variable pay. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
- The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The package is structured with a fixed component, performance bonus, and standard health coverage.
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
**About the role:**
- Position within AI Product Management, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for AI Product Management
**What you'll do:**
- Handle core ai product management activities on a day-to-day basis
- Use Python regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Python
- Model Evaluation & Tuning
- Data Preprocessing
- Feature Engineering
- Statistical Modelling
- AI Product Management
- Computer Vision/NLP Basics
- TensorFlow/PyTorch
- Machine Learning Algorithms
- M.Tech (Machine Learning)
- 8+ years of relevant experience
**Good to know:**
- There is a defined internal mobility path for people who want to specialise deeper or move into people management. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- The next logical step from this position is a senior or lead-track role within the same function. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Internal job postings are open to employees who've completed a minimum tenure in their current role.
- References may be requested closer to the offer stage rather than at the start of the process. Interested candidates should be prepared to walk through relevant past work during the interview process. High performers in this role typically move into a broader ownership scope within 18-24 months.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
- Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
- Compensation includes standard statutory benefits along with performance-linked variable pay. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
- The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The package is structured with a fixed component, performance bonus, and standard health coverage.
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.