Job Description
We are looking to onboard an AI Governance Architecture Lead to strengthen our AI Policy & Compliance function within Machine Learning. This is a senior level opening based out of Chennai, offered on a full-time basis.
The ideal candidate holds M.Tech (Machine Learning) and brings roughly 15 years of hands-on exposure to ai policy & compliance. Day-to-day, the work draws heavily on Data Preprocessing, TensorFlow/PyTorch, Computer Vision/NLP Basics, Feature Engineering, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 25.5-52.4 LPA, in line with market standards for senior level roles within machine learning.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. 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. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Learning and certification support is available for tools and frameworks directly relevant to the role. 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.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The next logical step from this position is a senior or lead-track role within the same function.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. High performers in this role typically move into a broader ownership scope within 18-24 months.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
The organisation has historically promoted from within for a majority of its senior and lead openings. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.
The ideal candidate holds M.Tech (Machine Learning) and brings roughly 15 years of hands-on exposure to ai policy & compliance. Day-to-day, the work draws heavily on Data Preprocessing, TensorFlow/PyTorch, Computer Vision/NLP Basics, Feature Engineering, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 25.5-52.4 LPA, in line with market standards for senior level roles within machine learning.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. 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. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Learning and certification support is available for tools and frameworks directly relevant to the role. 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.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The next logical step from this position is a senior or lead-track role within the same function.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. High performers in this role typically move into a broader ownership scope within 18-24 months.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
The organisation has historically promoted from within for a majority of its senior and lead openings. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.