Job Description
Machine Learning is looking for an experienced Head of MLOps Engineering to take ownership of the MLOps charter at a senior level. This is a role for someone who has already operated at scale and is comfortable setting direction rather than simply following one.
The successful candidate will bring approximately 16 years of progressively senior experience, holding Ph.D (ML/AI), with a demonstrated track record across TensorFlow/PyTorch, Deep Learning, Computer Vision/NLP Basics, Machine Learning Algorithms, Python. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Jaipur on a full-time arrangement. Compensation is benchmarked at INR 7.6-63.2 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within mlops.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Compensation includes standard statutory benefits along with performance-linked variable pay. The package is structured with a fixed component, performance bonus, and standard health coverage.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Internal job postings are open to employees who've completed a minimum tenure in their current role.
The next logical step from this position is a senior or lead-track role within the same function. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. 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. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. References may be requested closer to the offer stage rather than at the start of the process.
Interested candidates should be prepared to walk through relevant past work during the interview process. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
The successful candidate will bring approximately 16 years of progressively senior experience, holding Ph.D (ML/AI), with a demonstrated track record across TensorFlow/PyTorch, Deep Learning, Computer Vision/NLP Basics, Machine Learning Algorithms, Python. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Jaipur on a full-time arrangement. Compensation is benchmarked at INR 7.6-63.2 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within mlops.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Compensation includes standard statutory benefits along with performance-linked variable pay. The package is structured with a fixed component, performance bonus, and standard health coverage.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Internal job postings are open to employees who've completed a minimum tenure in their current role.
The next logical step from this position is a senior or lead-track role within the same function. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. 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. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. References may be requested closer to the offer stage rather than at the start of the process.
Interested candidates should be prepared to walk through relevant past work during the interview process. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.