Job Description
Here's the deal: we need an Enterprise MLOps Architect who can hit the ground running on MLOps work.
You'll be working out of Lucknow (hybrid), and honestly, the day-to-day is going to revolve around Model Evaluation & Tuning and MLOps. We're not looking for someone who just ticks boxes - we want someone who actually cares about mlops.
On paper, we'd expect M.Tech (Machine Learning) and somewhere around 12 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 31.7-68.1 LPA - fair for the senior level band and open to discussion based on what you bring.
Internal job postings are open to employees who've completed a minimum tenure in their current role. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Compensation includes standard statutory benefits along with performance-linked variable pay. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
The organisation has historically promoted from within for a majority of its senior and lead openings. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. 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.
Learning and certification support is available for tools and frameworks directly relevant to the role. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The package is structured with a fixed component, performance bonus, and standard health coverage. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Interested candidates should be prepared to walk through relevant past work during the interview process. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. The next logical step from this position is a senior or lead-track role within the same function. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
You'll be working out of Lucknow (hybrid), and honestly, the day-to-day is going to revolve around Model Evaluation & Tuning and MLOps. We're not looking for someone who just ticks boxes - we want someone who actually cares about mlops.
On paper, we'd expect M.Tech (Machine Learning) and somewhere around 12 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 31.7-68.1 LPA - fair for the senior level band and open to discussion based on what you bring.
Internal job postings are open to employees who've completed a minimum tenure in their current role. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Compensation includes standard statutory benefits along with performance-linked variable pay. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
The organisation has historically promoted from within for a majority of its senior and lead openings. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. 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.
Learning and certification support is available for tools and frameworks directly relevant to the role. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The package is structured with a fixed component, performance bonus, and standard health coverage. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Interested candidates should be prepared to walk through relevant past work during the interview process. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. The next logical step from this position is a senior or lead-track role within the same function. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.