Job Description
Here's the deal: we need a Deep Learning Architecture Engineer who can hit the ground running on Deep Learning work.
You'll be working out of Hybrid - Pune (contract), and honestly, the day-to-day is going to revolve around MLOps and Data Preprocessing. We're not looking for someone who just ticks boxes - we want someone who actually cares about deep learning.
On paper, we'd expect M.Sc (Data Science) and somewhere around 7 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 3.6-18.1 LPA - fair for the mid level band and open to discussion based on what you bring.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Learning and certification support is available for tools and frameworks directly relevant to the role. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Compensation includes standard statutory benefits along with performance-linked variable pay. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. References may be requested closer to the offer stage rather than at the start of the process.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
You'll be working out of Hybrid - Pune (contract), and honestly, the day-to-day is going to revolve around MLOps and Data Preprocessing. We're not looking for someone who just ticks boxes - we want someone who actually cares about deep learning.
On paper, we'd expect M.Sc (Data Science) and somewhere around 7 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 3.6-18.1 LPA - fair for the mid level band and open to discussion based on what you bring.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Learning and certification support is available for tools and frameworks directly relevant to the role. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Compensation includes standard statutory benefits along with performance-linked variable pay. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. References may be requested closer to the offer stage rather than at the start of the process.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.