Job Description
An opening exists for a MLOps Trainee to join the Machine Learning vertical, focused on MLOps. This is a junior level opening based out of Jaipur, offered on a full-time basis.
The ideal candidate holds B.Tech (CS/AI) and brings roughly 2 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on Statistical Modelling, Data Preprocessing, Python, Cloud Platforms (AWS/GCP), and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 4.5-13.4 LPA, in line with market standards for junior level roles within machine learning.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
Interested candidates should be prepared to walk through relevant past work during the interview process. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. The next logical step from this position is a senior or lead-track role within the same function.
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 team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Most days include at least one review checkpoint where progress is discussed against the plan for the week. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
If your background aligns with the above and you're looking for your next step in MLOps, we'd like to hear from you.
The ideal candidate holds B.Tech (CS/AI) and brings roughly 2 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on Statistical Modelling, Data Preprocessing, Python, Cloud Platforms (AWS/GCP), and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 4.5-13.4 LPA, in line with market standards for junior level roles within machine learning.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
Interested candidates should be prepared to walk through relevant past work during the interview process. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. The next logical step from this position is a senior or lead-track role within the same function.
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 team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Most days include at least one review checkpoint where progress is discussed against the plan for the week. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
If your background aligns with the above and you're looking for your next step in MLOps, we'd like to hear from you.