Job Description
Position: AI Compliance Technology Specialist
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: mid level
Location: Nagpur
Employment Type: Full-time (WFH)
Compensation: INR 17.6-32.3 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: 6 years
Core Competencies Required: Compliance, Computer Vision/NLP Basics, Deep Learning, Cloud Platforms (AWS/GCP), AI Policy, Statistical Modelling, Feature Engineering
Further Information:
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. 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. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Internal job postings are open to employees who've completed a minimum tenure in their current role. Relocation assistance may be extended for candidates moving from a different city, subject to approval. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The organisation has historically promoted from within for a majority of its senior and lead openings. There is a defined internal mobility path for people who want to specialise deeper or move into people management. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. The package is structured with a fixed component, performance bonus, and standard health coverage. Most days include at least one review checkpoint where progress is discussed against the plan for 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. Compensation includes standard statutory benefits along with performance-linked variable pay. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. High performers in this role typically move into a broader ownership scope within 18-24 months. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Learning and certification support is available for tools and frameworks directly relevant to the role.
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: mid level
Location: Nagpur
Employment Type: Full-time (WFH)
Compensation: INR 17.6-32.3 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: 6 years
Core Competencies Required: Compliance, Computer Vision/NLP Basics, Deep Learning, Cloud Platforms (AWS/GCP), AI Policy, Statistical Modelling, Feature Engineering
Further Information:
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. 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. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Internal job postings are open to employees who've completed a minimum tenure in their current role. Relocation assistance may be extended for candidates moving from a different city, subject to approval. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The organisation has historically promoted from within for a majority of its senior and lead openings. There is a defined internal mobility path for people who want to specialise deeper or move into people management. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. The package is structured with a fixed component, performance bonus, and standard health coverage. Most days include at least one review checkpoint where progress is discussed against the plan for 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. Compensation includes standard statutory benefits along with performance-linked variable pay. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. High performers in this role typically move into a broader ownership scope within 18-24 months. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Learning and certification support is available for tools and frameworks directly relevant to the role.