Job Description
AI Governance Strategy Lead | Kolkata | INR 46.4-99.5 LPA
AI Policy & Compliance role, senior level, full-time.
Need: M.Tech (Machine Learning), 11+ yrs.
Core skills: TensorFlow/PyTorch, Computer Vision/NLP Basics, Deep Learning, Compliance, AI Policy.
If this fits your profile in ai policy & compliance, apply now.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Learning and certification support is available for tools and frameworks directly relevant to the role.
Interested candidates should be prepared to walk through relevant past work during the interview process. References may be requested closer to the offer stage rather than at the start of the process.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The package is structured with a fixed component, performance bonus, and standard health coverage. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
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 organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
AI Policy & Compliance role, senior level, full-time.
Need: M.Tech (Machine Learning), 11+ yrs.
Core skills: TensorFlow/PyTorch, Computer Vision/NLP Basics, Deep Learning, Compliance, AI Policy.
If this fits your profile in ai policy & compliance, apply now.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Learning and certification support is available for tools and frameworks directly relevant to the role.
Interested candidates should be prepared to walk through relevant past work during the interview process. References may be requested closer to the offer stage rather than at the start of the process.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The package is structured with a fixed component, performance bonus, and standard health coverage. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
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 organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.