Job Description
Here's the deal: we need a Deep Learning Implementation Specialist who can hit the ground running on Deep Learning work.
You'll be working out of Nagpur (full-time), and honestly, the day-to-day is going to revolve around Computer Vision/NLP Basics and Python. 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.Tech (Machine Learning) 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 25.7-55.5 LPA - fair for the mid level band and open to discussion based on what you bring.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
Interested candidates should be prepared to walk through relevant past work during the interview process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The organisation has historically promoted from within for a majority of its senior and lead openings.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The next logical step from this position is a senior or lead-track role within the same function.
High performers in this role typically move into a broader ownership scope within 18-24 months. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. References may be requested closer to the offer stage rather than at the start of the process.
Compensation includes standard statutory benefits along with performance-linked variable pay. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Learning and certification support is available for tools and frameworks directly relevant to the role. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The package is structured with a fixed component, performance bonus, and standard health coverage.
You'll be working out of Nagpur (full-time), and honestly, the day-to-day is going to revolve around Computer Vision/NLP Basics and Python. 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.Tech (Machine Learning) 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 25.7-55.5 LPA - fair for the mid level band and open to discussion based on what you bring.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
Interested candidates should be prepared to walk through relevant past work during the interview process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The organisation has historically promoted from within for a majority of its senior and lead openings.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The next logical step from this position is a senior or lead-track role within the same function.
High performers in this role typically move into a broader ownership scope within 18-24 months. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. References may be requested closer to the offer stage rather than at the start of the process.
Compensation includes standard statutory benefits along with performance-linked variable pay. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Learning and certification support is available for tools and frameworks directly relevant to the role. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The package is structured with a fixed component, performance bonus, and standard health coverage.