Job Description
The organisation is seeking a qualified Principal MLOps Engineer to support ongoing initiatives in MLOps. This is a senior level opening based out of Chandigarh, offered on a hybrid basis.
The ideal candidate holds Ph.D (ML/AI) and brings roughly 9 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on TensorFlow/PyTorch, Feature Engineering, MLOps, Model Evaluation & Tuning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 40.9-92.7 LPA, in line with market standards for senior level roles within machine learning.
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 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.
The next logical step from this position is a senior or lead-track role within the same function. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The package is structured with a fixed component, performance bonus, and standard health coverage. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Compensation includes standard statutory benefits along with performance-linked variable pay. 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.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.
The ideal candidate holds Ph.D (ML/AI) and brings roughly 9 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on TensorFlow/PyTorch, Feature Engineering, MLOps, Model Evaluation & Tuning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 40.9-92.7 LPA, in line with market standards for senior level roles within machine learning.
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 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.
The next logical step from this position is a senior or lead-track role within the same function. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The package is structured with a fixed component, performance bonus, and standard health coverage. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Compensation includes standard statutory benefits along with performance-linked variable pay. 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.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.