Job Description
Position: AI Policy Optimization Specialist
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: mid level
Location: Chennai
Employment Type: Full-time
Compensation: INR 22.6-34.8 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: B.Tech (CS/AI)
Minimum Experience: 3 years
Core Competencies Required: Computer Vision/NLP Basics, Python, AI Policy, Compliance, TensorFlow/PyTorch, Feature Engineering
Further Information:
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. 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. 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. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. The organisation has historically promoted from within for a majority of its senior and lead openings. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Learning and certification support is available for tools and frameworks directly relevant to the role. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: mid level
Location: Chennai
Employment Type: Full-time
Compensation: INR 22.6-34.8 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: B.Tech (CS/AI)
Minimum Experience: 3 years
Core Competencies Required: Computer Vision/NLP Basics, Python, AI Policy, Compliance, TensorFlow/PyTorch, Feature Engineering
Further Information:
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. 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. 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. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. The organisation has historically promoted from within for a majority of its senior and lead openings. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Learning and certification support is available for tools and frameworks directly relevant to the role. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.