Job Description
An opening exists for a Chair Professor – Machine Learning to join the Academic Research vertical, focused on Artificial Intelligence & Machine Learning. This is a senior level opening based out of Indore, offered on a leadership - full-time basis.
The ideal candidate holds Ph.D and brings roughly 12 years of hands-on exposure to artificial intelligence & machine learning. Day-to-day, the work draws heavily on Literature Review, Experimental Design, Presentation Skills, Artificial Intelligence, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 11.1-35.2 LPA, in line with market standards for senior level roles within academic research.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The organisation has historically promoted from within for a majority of its senior and lead openings.
Compensation includes standard statutory benefits along with performance-linked variable pay. The next logical step from this position is a senior or lead-track role within the same function. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Learning and certification support is available for tools and frameworks directly relevant to the role. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
High performers in this role typically move into a broader ownership scope within 18-24 months. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
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. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The package is structured with a fixed component, performance bonus, and standard health coverage.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Interested candidates should be prepared to walk through relevant past work during the interview process. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
References may be requested closer to the offer stage rather than at the start of the process. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
If your background aligns with the above and you're looking for your next step in Artificial Intelligence & Machine Learning, we'd like to hear from you.
The ideal candidate holds Ph.D and brings roughly 12 years of hands-on exposure to artificial intelligence & machine learning. Day-to-day, the work draws heavily on Literature Review, Experimental Design, Presentation Skills, Artificial Intelligence, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 11.1-35.2 LPA, in line with market standards for senior level roles within academic research.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The organisation has historically promoted from within for a majority of its senior and lead openings.
Compensation includes standard statutory benefits along with performance-linked variable pay. The next logical step from this position is a senior or lead-track role within the same function. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Learning and certification support is available for tools and frameworks directly relevant to the role. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
High performers in this role typically move into a broader ownership scope within 18-24 months. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
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. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The package is structured with a fixed component, performance bonus, and standard health coverage.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Interested candidates should be prepared to walk through relevant past work during the interview process. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
References may be requested closer to the offer stage rather than at the start of the process. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
If your background aligns with the above and you're looking for your next step in Artificial Intelligence & Machine Learning, we'd like to hear from you.