Job Description
**MLOps Solutions Engineer** | Mumbai | Full-time (WFH) | INR 26.1-37.8 LPA
**About the role:**
- Position within MLOps, under the Machine Learning vertical
- Level: mid level
- Reports to the functional head for MLOps
**What you'll do:**
- Handle core mlops activities on a day-to-day basis
- Use Python regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Python
- Feature Engineering
- MLOps
- Model Evaluation & Tuning
- Cloud Platforms (AWS/GCP)
- Data Preprocessing
- Statistical Modelling
- M.Sc (Data Science)
- 5+ years of relevant experience
**Good to know:**
- Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
- Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
- Compensation includes standard statutory benefits along with performance-linked variable pay. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- Internal job postings are open to employees who've completed a minimum tenure in their current role. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
- Learning and certification support is available for tools and frameworks directly relevant to the role. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. 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.
- Interested candidates should be prepared to walk through relevant past work during the interview process. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
- Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- The package is structured with a fixed component, performance bonus, and standard health coverage. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
- Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The next logical step from this position is a senior or lead-track role within the same function.
**About the role:**
- Position within MLOps, under the Machine Learning vertical
- Level: mid level
- Reports to the functional head for MLOps
**What you'll do:**
- Handle core mlops activities on a day-to-day basis
- Use Python regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Python
- Feature Engineering
- MLOps
- Model Evaluation & Tuning
- Cloud Platforms (AWS/GCP)
- Data Preprocessing
- Statistical Modelling
- M.Sc (Data Science)
- 5+ years of relevant experience
**Good to know:**
- Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
- Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
- Compensation includes standard statutory benefits along with performance-linked variable pay. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- Internal job postings are open to employees who've completed a minimum tenure in their current role. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
- Learning and certification support is available for tools and frameworks directly relevant to the role. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. 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.
- Interested candidates should be prepared to walk through relevant past work during the interview process. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
- Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- The package is structured with a fixed component, performance bonus, and standard health coverage. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
- Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The next logical step from this position is a senior or lead-track role within the same function.