Job Description
Artificial Intelligence (AI) is looking for an experienced AI Risk Program Director to take ownership of the AI-driven Risk Management charter at a senior 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 16 years of progressively senior experience, holding M.Sc (Data Science), with a demonstrated track record across Statistical Modelling, Machine Learning, Python, Cloud Platforms (AWS/GCP/Azure), TensorFlow/PyTorch. 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 Jaipur on a full-time arrangement. Compensation is benchmarked at INR 47.9-80.6 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-driven risk management.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The organisation has historically promoted from within for a majority of its senior and lead openings. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. The next logical step from this position is a senior or lead-track role within the same function.
References may be requested closer to the offer stage rather than at the start of the process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Interested candidates should be prepared to walk through relevant past work during the interview 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.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Compensation includes standard statutory benefits along with performance-linked variable pay.
The successful candidate will bring approximately 16 years of progressively senior experience, holding M.Sc (Data Science), with a demonstrated track record across Statistical Modelling, Machine Learning, Python, Cloud Platforms (AWS/GCP/Azure), TensorFlow/PyTorch. 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 Jaipur on a full-time arrangement. Compensation is benchmarked at INR 47.9-80.6 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-driven risk management.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The organisation has historically promoted from within for a majority of its senior and lead openings. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. The next logical step from this position is a senior or lead-track role within the same function.
References may be requested closer to the offer stage rather than at the start of the process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Interested candidates should be prepared to walk through relevant past work during the interview 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.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Compensation includes standard statutory benefits along with performance-linked variable pay.