Job Description
An opening exists for a Global Deep Learning Solutions Architect to join the Machine Learning vertical, focused on Deep Learning. This is a senior level opening based out of Delhi NCR, offered on a hybrid basis.
The ideal candidate holds M.Sc (Data Science) and brings roughly 13 years of hands-on exposure to deep learning. Day-to-day, the work draws heavily on Data Preprocessing, Deep Learning, Computer Vision/NLP Basics, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 38.4-100.7 LPA, in line with market standards for senior level roles within machine learning.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Interested candidates should be prepared to walk through relevant past work during the interview process.
The package is structured with a fixed component, performance bonus, and standard health coverage. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. References may be requested closer to the offer stage rather than at the start of the process.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The next logical step from this position is a senior or lead-track role within the same function.
The organisation has historically promoted from within for a majority of its senior and lead openings. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
We value consistency and follow-through more than flashy credentials - if that matches how you work, apply in.
The ideal candidate holds M.Sc (Data Science) and brings roughly 13 years of hands-on exposure to deep learning. Day-to-day, the work draws heavily on Data Preprocessing, Deep Learning, Computer Vision/NLP Basics, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 38.4-100.7 LPA, in line with market standards for senior level roles within machine learning.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Interested candidates should be prepared to walk through relevant past work during the interview process.
The package is structured with a fixed component, performance bonus, and standard health coverage. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. References may be requested closer to the offer stage rather than at the start of the process.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The next logical step from this position is a senior or lead-track role within the same function.
The organisation has historically promoted from within for a majority of its senior and lead openings. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
We value consistency and follow-through more than flashy credentials - if that matches how you work, apply in.