Job Description
Machine Learning Innovation Lead | Nagpur | INR 17.8-45.6 LPA
Machine Learning Engineering role, senior level, full-time.
Need: M.Tech (Machine Learning), 15+ yrs.
Core skills: TensorFlow/PyTorch, Python, Machine Learning Algorithms, Machine Learning Engineering, Data Preprocessing.
If this fits your profile in machine learning engineering, apply now.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Internal job postings are open to employees who've completed a minimum tenure in their current role. 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 organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Compensation includes standard statutory benefits along with performance-linked variable pay.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
High performers in this role typically move into a broader ownership scope within 18-24 months. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Interested candidates should be prepared to walk through relevant past work during the interview process. The next logical step from this position is a senior or lead-track role within the same function. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
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. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The organisation has historically promoted from within for a majority of its senior and lead openings. References may be requested closer to the offer stage rather than at the start of the process.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The package is structured with a fixed component, performance bonus, and standard health coverage.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Machine Learning Engineering role, senior level, full-time.
Need: M.Tech (Machine Learning), 15+ yrs.
Core skills: TensorFlow/PyTorch, Python, Machine Learning Algorithms, Machine Learning Engineering, Data Preprocessing.
If this fits your profile in machine learning engineering, apply now.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Internal job postings are open to employees who've completed a minimum tenure in their current role. 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 organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Compensation includes standard statutory benefits along with performance-linked variable pay.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
High performers in this role typically move into a broader ownership scope within 18-24 months. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Interested candidates should be prepared to walk through relevant past work during the interview process. The next logical step from this position is a senior or lead-track role within the same function. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
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. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The organisation has historically promoted from within for a majority of its senior and lead openings. References may be requested closer to the offer stage rather than at the start of the process.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The package is structured with a fixed component, performance bonus, and standard health coverage.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.