Job Description
Position: ML Model Production Engineer
Function: MLOps
Industry: Machine Learning
Grade: mid level
Location: Indore
Employment Type: Full-time
Compensation: INR 20.4-31.7 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with mlops, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 7 years
Core Competencies Required: Deep Learning, MLOps, Python, Model Evaluation & Tuning, Feature Engineering, Computer Vision/NLP Basics
Further Information:
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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Learning and certification support is available for tools and frameworks directly relevant to the role. 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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Interested candidates should be prepared to walk through relevant past work during the interview process. Compensation includes standard statutory benefits along with performance-linked variable pay. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Function: MLOps
Industry: Machine Learning
Grade: mid level
Location: Indore
Employment Type: Full-time
Compensation: INR 20.4-31.7 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with mlops, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 7 years
Core Competencies Required: Deep Learning, MLOps, Python, Model Evaluation & Tuning, Feature Engineering, Computer Vision/NLP Basics
Further Information:
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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Learning and certification support is available for tools and frameworks directly relevant to the role. 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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Interested candidates should be prepared to walk through relevant past work during the interview process. Compensation includes standard statutory benefits along with performance-linked variable pay. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.