Job Description
The organisation is seeking a qualified ML Lifecycle Management Engineer to support ongoing initiatives in MLOps. This is a mid level opening based out of Kochi, offered on a full-time basis.
The ideal candidate holds M.Tech (Machine Learning) and brings roughly 4 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on Cloud Platforms (AWS/GCP), Computer Vision/NLP Basics, TensorFlow/PyTorch, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 16.7-29.6 LPA, in line with market standards for mid level roles within machine learning.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
Internal job postings are open to employees who've completed a minimum tenure in their current role. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The next logical step from this position is a senior or lead-track role within the same function. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
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.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.
The ideal candidate holds M.Tech (Machine Learning) and brings roughly 4 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on Cloud Platforms (AWS/GCP), Computer Vision/NLP Basics, TensorFlow/PyTorch, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 16.7-29.6 LPA, in line with market standards for mid level roles within machine learning.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
Internal job postings are open to employees who've completed a minimum tenure in their current role. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The next logical step from this position is a senior or lead-track role within the same function. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
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.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.