Job Description
Machine Learning Engineering Manager | Bhubaneswar | INR 25.2-51.6 LPA
Machine Learning Engineering role, senior level, hybrid.
Need: M.Tech (Machine Learning), 14+ yrs.
Core skills: Machine Learning Engineering, MLOps, Deep Learning, TensorFlow/PyTorch, Data Preprocessing.
If this fits your profile in machine learning engineering, apply now.
Learning and certification support is available for tools and frameworks directly relevant to the role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The package is structured with a fixed component, performance bonus, and standard health coverage.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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.
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.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. References may be requested closer to the offer stage rather than at the start of the process.
The next logical step from this position is a senior or lead-track role within the same function. Interested candidates should be prepared to walk through relevant past work during the interview process.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. 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. 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.
Machine Learning Engineering role, senior level, hybrid.
Need: M.Tech (Machine Learning), 14+ yrs.
Core skills: Machine Learning Engineering, MLOps, Deep Learning, TensorFlow/PyTorch, Data Preprocessing.
If this fits your profile in machine learning engineering, apply now.
Learning and certification support is available for tools and frameworks directly relevant to the role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The package is structured with a fixed component, performance bonus, and standard health coverage.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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.
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.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. References may be requested closer to the offer stage rather than at the start of the process.
The next logical step from this position is a senior or lead-track role within the same function. Interested candidates should be prepared to walk through relevant past work during the interview process.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. 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. 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.