Job Description
Machine Learning is looking for an experienced AI Compliance Systems Engineer to take ownership of the AI Policy & Compliance charter at a mid level. This is a role for someone who has already operated at scale and is comfortable setting direction rather than simply following one.
The successful candidate will bring approximately 4 years of progressively senior experience, holding B.Tech (CS/AI), with a demonstrated track record across MLOps, Machine Learning Algorithms, TensorFlow/PyTorch, Feature Engineering, Compliance. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Ahmedabad on a full-time arrangement. Compensation is benchmarked at INR 13.2-36.7 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within ai policy & compliance.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. High performers in this role typically move into a broader ownership scope within 18-24 months.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
The next logical step from this position is a senior or lead-track role within the same function. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. 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.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Internal job postings are open to employees who've completed a minimum tenure in their current role.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Compensation includes standard statutory benefits along with performance-linked variable pay.
The successful candidate will bring approximately 4 years of progressively senior experience, holding B.Tech (CS/AI), with a demonstrated track record across MLOps, Machine Learning Algorithms, TensorFlow/PyTorch, Feature Engineering, Compliance. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Ahmedabad on a full-time arrangement. Compensation is benchmarked at INR 13.2-36.7 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within ai policy & compliance.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. High performers in this role typically move into a broader ownership scope within 18-24 months.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
The next logical step from this position is a senior or lead-track role within the same function. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. 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.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Internal job postings are open to employees who've completed a minimum tenure in their current role.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Compensation includes standard statutory benefits along with performance-linked variable pay.