Job Description
The organisation is seeking a qualified ML Deployment Solutions Engineer to support ongoing initiatives in MLOps. This is a mid level opening based out of Remote (Pan India), offered on a full-time basis.
The ideal candidate holds M.Tech (Machine Learning) and brings roughly 3 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on MLOps, TensorFlow/PyTorch, Python, Model Evaluation & Tuning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 19.8-47.4 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. References may be requested closer to the offer stage rather than at the start of the process.
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. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. 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. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
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 3 years of hands-on exposure to mlops. Day-to-day, the work draws heavily on MLOps, TensorFlow/PyTorch, Python, Model Evaluation & Tuning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 19.8-47.4 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. References may be requested closer to the offer stage rather than at the start of the process.
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. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. 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. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.