Job Description
An opening exists for an AI Cyber Defense Engineer to join the Machine Learning vertical, focused on AI for Cybersecurity. This is a mid level opening based out of Hybrid - Pune, offered on a hybrid basis.
The ideal candidate holds M.Tech (Machine Learning) and brings roughly 6 years of hands-on exposure to ai for cybersecurity. Day-to-day, the work draws heavily on Deep Learning, Python, AI for Cybersecurity, 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 6.0-17.9 LPA, in line with market standards for mid level roles within machine learning.
High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Interested candidates should be prepared to walk through relevant past work during the interview process.
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.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
If your background aligns with the above and you're looking for your next step in AI for Cybersecurity, we'd like to hear from you.
The ideal candidate holds M.Tech (Machine Learning) and brings roughly 6 years of hands-on exposure to ai for cybersecurity. Day-to-day, the work draws heavily on Deep Learning, Python, AI for Cybersecurity, 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 6.0-17.9 LPA, in line with market standards for mid level roles within machine learning.
High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Interested candidates should be prepared to walk through relevant past work during the interview process.
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.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
If your background aligns with the above and you're looking for your next step in AI for Cybersecurity, we'd like to hear from you.