Job Description
MLOps Framework Engineer | Remote (Pan India) | INR 26.9-50.1 LPA
MLOps role, mid level, full-time.
Need: M.Tech (Machine Learning), 4+ yrs.
Core skills: Feature Engineering, MLOps, Deep Learning, Computer Vision/NLP Basics, Statistical Modelling.
If this fits your profile in mlops, apply now.
High performers in this role typically move into a broader ownership scope within 18-24 months. The next logical step from this position is a senior or lead-track role within the same function. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
References may be requested closer to the offer stage rather than at the start of the process. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Compensation includes standard statutory benefits along with performance-linked variable pay. 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. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Internal job postings are open to employees who've completed a minimum tenure in their current role. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview 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. The package is structured with a fixed component, performance bonus, and standard health coverage. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Learning and certification support is available for tools and frameworks directly relevant to the role.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
MLOps role, mid level, full-time.
Need: M.Tech (Machine Learning), 4+ yrs.
Core skills: Feature Engineering, MLOps, Deep Learning, Computer Vision/NLP Basics, Statistical Modelling.
If this fits your profile in mlops, apply now.
High performers in this role typically move into a broader ownership scope within 18-24 months. The next logical step from this position is a senior or lead-track role within the same function. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
References may be requested closer to the offer stage rather than at the start of the process. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Compensation includes standard statutory benefits along with performance-linked variable pay. 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. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Internal job postings are open to employees who've completed a minimum tenure in their current role. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview 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. The package is structured with a fixed component, performance bonus, and standard health coverage. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Learning and certification support is available for tools and frameworks directly relevant to the role.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.