Job Description
## ML Lifecycle Strategy Lead
**Department:** MLOps | **Industry:** Machine Learning
**Location:** Lucknow | **Job Type:** Full-time | **CTC:** INR 12.0-64.6 LPA
### Role Overview
We are looking to onboard a ML Lifecycle Strategy Lead to strengthen our MLOps function within Machine Learning. The role is positioned at the senior level and reports into the broader MLOps charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to mlops.
- Apply working knowledge of TensorFlow/PyTorch and Statistical Modelling to daily execution.
- Coordinate with cross-functional stakeholders within Machine Learning.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Tech (Machine Learning) or equivalent qualification.
- 15 years of relevant experience (senior level).
- Working proficiency in: TensorFlow/PyTorch, Statistical Modelling, Python, Data Preprocessing, MLOps, Feature Engineering, Cloud Platforms (AWS/GCP), Computer Vision/NLP Basics.
### Additional Notes
References may be requested closer to the offer stage rather than at the start of the process. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Internal job postings are open to employees who've completed a minimum tenure in their current role. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. 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. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. High performers in this role typically move into a broader ownership scope within 18-24 months. There is a defined internal mobility path for people who want to specialise deeper or move into people management. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The organisation has historically promoted from within for a majority of its senior and lead openings. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Learning and certification support is available for tools and frameworks directly relevant to the role. 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. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. 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. The package is structured with a fixed component, performance bonus, and standard health coverage. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
**Department:** MLOps | **Industry:** Machine Learning
**Location:** Lucknow | **Job Type:** Full-time | **CTC:** INR 12.0-64.6 LPA
### Role Overview
We are looking to onboard a ML Lifecycle Strategy Lead to strengthen our MLOps function within Machine Learning. The role is positioned at the senior level and reports into the broader MLOps charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to mlops.
- Apply working knowledge of TensorFlow/PyTorch and Statistical Modelling to daily execution.
- Coordinate with cross-functional stakeholders within Machine Learning.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Tech (Machine Learning) or equivalent qualification.
- 15 years of relevant experience (senior level).
- Working proficiency in: TensorFlow/PyTorch, Statistical Modelling, Python, Data Preprocessing, MLOps, Feature Engineering, Cloud Platforms (AWS/GCP), Computer Vision/NLP Basics.
### Additional Notes
References may be requested closer to the offer stage rather than at the start of the process. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Internal job postings are open to employees who've completed a minimum tenure in their current role. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. 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. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. High performers in this role typically move into a broader ownership scope within 18-24 months. There is a defined internal mobility path for people who want to specialise deeper or move into people management. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The organisation has historically promoted from within for a majority of its senior and lead openings. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Learning and certification support is available for tools and frameworks directly relevant to the role. 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. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. 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. The package is structured with a fixed component, performance bonus, and standard health coverage. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.