Job Description
We're hiring an AI Risk Governance Manager to join our growing AI-driven Risk Management team - if this sounds like your kind of work, read on.
You'll be working out of Delhi NCR (leadership - full-time), and honestly, the day-to-day is going to revolve around Neural Network Architectures and Model Deployment (MLOps). We're not looking for someone who just ticks boxes - we want someone who actually cares about ai-driven risk management.
On paper, we'd expect Ph.D (AI) and somewhere around 14 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 8.1-30.2 LPA - fair for the senior level band and open to discussion based on what you bring.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Interested candidates should be prepared to walk through relevant past work during the interview process.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The organisation has historically promoted from within for a majority of its senior and lead openings.
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.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The next logical step from this position is a senior or lead-track role within the same function. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
You'll be working out of Delhi NCR (leadership - full-time), and honestly, the day-to-day is going to revolve around Neural Network Architectures and Model Deployment (MLOps). We're not looking for someone who just ticks boxes - we want someone who actually cares about ai-driven risk management.
On paper, we'd expect Ph.D (AI) and somewhere around 14 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 8.1-30.2 LPA - fair for the senior level band and open to discussion based on what you bring.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Interested candidates should be prepared to walk through relevant past work during the interview process.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The organisation has historically promoted from within for a majority of its senior and lead openings.
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.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The next logical step from this position is a senior or lead-track role within the same function. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.