Job Description
**Professor of Artificial Intelligence** | Bhubaneswar | Full-time | INR 7.9-36.3 LPA
**About the role:**
- Position within Artificial Intelligence & Machine Learning, under the Academic Research vertical
- Level: senior level
- Reports to the functional head for Artificial Intelligence & Machine Learning
**What you'll do:**
- Handle core artificial intelligence & machine learning activities on a day-to-day basis
- Use Citation Management (EndNote/Zotero) 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:**
- Citation Management (EndNote/Zotero)
- Research Ethics
- Artificial Intelligence
- Data Analysis
- Statistical Software (SPSS/R)
- Machine Learning
- Peer Review
- Ph.D
- 10+ years of relevant experience
**Good to know:**
- Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
- 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. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. 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.
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Interested candidates should be prepared to walk through relevant past work during the interview process.
- Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
- Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Compensation includes standard statutory benefits along with performance-linked variable pay.
**About the role:**
- Position within Artificial Intelligence & Machine Learning, under the Academic Research vertical
- Level: senior level
- Reports to the functional head for Artificial Intelligence & Machine Learning
**What you'll do:**
- Handle core artificial intelligence & machine learning activities on a day-to-day basis
- Use Citation Management (EndNote/Zotero) 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:**
- Citation Management (EndNote/Zotero)
- Research Ethics
- Artificial Intelligence
- Data Analysis
- Statistical Software (SPSS/R)
- Machine Learning
- Peer Review
- Ph.D
- 10+ years of relevant experience
**Good to know:**
- Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
- 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. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. 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.
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Interested candidates should be prepared to walk through relevant past work during the interview process.
- Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
- Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Compensation includes standard statutory benefits along with performance-linked variable pay.