Job Description
The organisation is seeking a qualified Head – AI & Deep Learning to support ongoing initiatives in Deep Learning. This is a senior level opening based out of Ahmedabad, offered on a full-time basis.
The ideal candidate holds M.Sc (Data Science) and brings roughly 9 years of hands-on exposure to deep learning. Day-to-day, the work draws heavily on Deep Learning, Model Deployment (MLOps), Cloud Platforms (AWS/GCP/Azure), Data Preprocessing, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 36.7-81.9 LPA, in line with market standards for senior level roles within artificial intelligence (ai).
There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Learning and certification support is available for tools and frameworks directly relevant to the role. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
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.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Compensation includes standard statutory benefits along with performance-linked variable pay.
The package is structured with a fixed component, performance bonus, and standard health coverage. 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. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. 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. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. 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. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.
The ideal candidate holds M.Sc (Data Science) and brings roughly 9 years of hands-on exposure to deep learning. Day-to-day, the work draws heavily on Deep Learning, Model Deployment (MLOps), Cloud Platforms (AWS/GCP/Azure), Data Preprocessing, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 36.7-81.9 LPA, in line with market standards for senior level roles within artificial intelligence (ai).
There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Learning and certification support is available for tools and frameworks directly relevant to the role. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
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.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Compensation includes standard statutory benefits along with performance-linked variable pay.
The package is structured with a fixed component, performance bonus, and standard health coverage. 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. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. 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. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. 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. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.