Job Description
Position: Deep Learning Solutions Engineer
Function: Deep Learning
Industry: Machine Learning
Grade: mid level
Location: Ahmedabad
Employment Type: Full-time
Compensation: INR 12.6-25.2 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with deep learning, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Sc (Data Science)
Minimum Experience: 5 years
Core Competencies Required: Python, Data Preprocessing, Statistical Modelling, Feature Engineering, Deep Learning, Model Evaluation & Tuning
Further Information:
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The package is structured with a fixed component, performance bonus, and standard health coverage. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. High performers in this role typically move into a broader ownership scope within 18-24 months. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. References may be requested closer to the offer stage rather than at the start of the process. The organisation has historically promoted from within for a majority of its senior and lead openings. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. 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. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. There is a defined internal mobility path for people who want to specialise deeper or move into people management. 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.
Function: Deep Learning
Industry: Machine Learning
Grade: mid level
Location: Ahmedabad
Employment Type: Full-time
Compensation: INR 12.6-25.2 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with deep learning, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Sc (Data Science)
Minimum Experience: 5 years
Core Competencies Required: Python, Data Preprocessing, Statistical Modelling, Feature Engineering, Deep Learning, Model Evaluation & Tuning
Further Information:
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The package is structured with a fixed component, performance bonus, and standard health coverage. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. High performers in this role typically move into a broader ownership scope within 18-24 months. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. References may be requested closer to the offer stage rather than at the start of the process. The organisation has historically promoted from within for a majority of its senior and lead openings. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. 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. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. There is a defined internal mobility path for people who want to specialise deeper or move into people management. 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.