Job Description
Position: AI Policy Advisory Consultant
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: mid level
Location: Jaipur
Employment Type: Full-time
Compensation: INR 23.1-47.1 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai policy & compliance, 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: Machine Learning Algorithms, Compliance, TensorFlow/PyTorch, Data Preprocessing, AI Policy, MLOps, Cloud Platforms (AWS/GCP), Model Evaluation & Tuning
Further Information:
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The package is structured with a fixed component, performance bonus, and standard health coverage. Learning and certification support is available for tools and frameworks directly relevant to the role. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The next logical step from this position is a senior or lead-track role within the same function. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Compensation includes standard statutory benefits along with performance-linked variable pay. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. 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. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. 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. 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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. References may be requested closer to the offer stage rather than at the start of the process. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. Internal job postings are open to employees who've completed a minimum tenure in their current role. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: mid level
Location: Jaipur
Employment Type: Full-time
Compensation: INR 23.1-47.1 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai policy & compliance, 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: Machine Learning Algorithms, Compliance, TensorFlow/PyTorch, Data Preprocessing, AI Policy, MLOps, Cloud Platforms (AWS/GCP), Model Evaluation & Tuning
Further Information:
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The package is structured with a fixed component, performance bonus, and standard health coverage. Learning and certification support is available for tools and frameworks directly relevant to the role. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The next logical step from this position is a senior or lead-track role within the same function. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Compensation includes standard statutory benefits along with performance-linked variable pay. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. 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. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. 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. 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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. References may be requested closer to the offer stage rather than at the start of the process. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. Internal job postings are open to employees who've completed a minimum tenure in their current role. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.