Job Description
Position: AI Product Engineering Director
Function: AI Product Management
Industry: Machine Learning
Grade: senior level
Location: Nagpur
Employment Type: Full-time
Compensation: INR 30.1-70.5 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai product management, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 8 years
Core Competencies Required: TensorFlow/PyTorch, Deep Learning, Feature Engineering, Model Evaluation & Tuning, Python, Cloud Platforms (AWS/GCP), AI Product Management
Further Information:
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The package is structured with a fixed component, performance bonus, and standard health coverage. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview process. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval. The next logical step from this position is a senior or lead-track role within the same function. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Compensation includes standard statutory benefits along with performance-linked variable pay. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The organisation has historically promoted from within for a majority of its senior and lead openings. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. References may be requested closer to the offer stage rather than at the start of the process. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Internal job postings are open to employees who've completed a minimum tenure in their current role. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. 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.
Function: AI Product Management
Industry: Machine Learning
Grade: senior level
Location: Nagpur
Employment Type: Full-time
Compensation: INR 30.1-70.5 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai product management, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 8 years
Core Competencies Required: TensorFlow/PyTorch, Deep Learning, Feature Engineering, Model Evaluation & Tuning, Python, Cloud Platforms (AWS/GCP), AI Product Management
Further Information:
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The package is structured with a fixed component, performance bonus, and standard health coverage. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview process. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval. The next logical step from this position is a senior or lead-track role within the same function. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Compensation includes standard statutory benefits along with performance-linked variable pay. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The organisation has historically promoted from within for a majority of its senior and lead openings. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. References may be requested closer to the offer stage rather than at the start of the process. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Internal job postings are open to employees who've completed a minimum tenure in their current role. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. 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.