Job Description
Position: AI Security Infrastructure Engineer
Function: AI for Cybersecurity
Industry: Machine Learning
Grade: mid level
Location: Nagpur
Employment Type: Contract
Compensation: INR 12.2-26.2 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai for cybersecurity, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 5 years
Core Competencies Required: AI for Cybersecurity, Cloud Platforms (AWS/GCP), Data Preprocessing, Python, TensorFlow/PyTorch
Further Information:
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Internal job postings are open to employees who've completed a minimum tenure in their current role. The package is structured with a fixed component, performance bonus, and standard health coverage. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. References may be requested closer to the offer stage rather than at the start of the process. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Compensation includes standard statutory benefits along with performance-linked variable pay. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. 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. 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.
Function: AI for Cybersecurity
Industry: Machine Learning
Grade: mid level
Location: Nagpur
Employment Type: Contract
Compensation: INR 12.2-26.2 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai for cybersecurity, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 5 years
Core Competencies Required: AI for Cybersecurity, Cloud Platforms (AWS/GCP), Data Preprocessing, Python, TensorFlow/PyTorch
Further Information:
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Internal job postings are open to employees who've completed a minimum tenure in their current role. The package is structured with a fixed component, performance bonus, and standard health coverage. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. References may be requested closer to the offer stage rather than at the start of the process. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Compensation includes standard statutory benefits along with performance-linked variable pay. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. 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. 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.