Job Description
**Advanced AI Governance Systems Architect** | Hybrid - Bengaluru | Full-time | INR 50.8-76.2 LPA
**About the role:**
- Position within AI Policy & Compliance, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for AI Policy & Compliance
**What you'll do:**
- Handle core ai policy & compliance activities on a day-to-day basis
- Use AI Policy regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- AI Policy
- MLOps
- Model Evaluation & Tuning
- Data Preprocessing
- Feature Engineering
- TensorFlow/PyTorch
- Compliance
- Statistical Modelling
- Computer Vision/NLP Basics
- M.Tech (Machine Learning)
- 10+ years of relevant experience
**Good to know:**
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. 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.
- Most days include at least one review checkpoint where progress is discussed against the plan for the week. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Internal job postings are open to employees who've completed a minimum tenure in their current role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
- References may be requested closer to the offer stage rather than at the start of the process. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- The organisation has historically promoted from within for a majority of its senior and lead openings. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Compensation includes standard statutory benefits along with performance-linked variable pay.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
- In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
- Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
**About the role:**
- Position within AI Policy & Compliance, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for AI Policy & Compliance
**What you'll do:**
- Handle core ai policy & compliance activities on a day-to-day basis
- Use AI Policy regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- AI Policy
- MLOps
- Model Evaluation & Tuning
- Data Preprocessing
- Feature Engineering
- TensorFlow/PyTorch
- Compliance
- Statistical Modelling
- Computer Vision/NLP Basics
- M.Tech (Machine Learning)
- 10+ years of relevant experience
**Good to know:**
- The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. 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.
- Most days include at least one review checkpoint where progress is discussed against the plan for the week. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Internal job postings are open to employees who've completed a minimum tenure in their current role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
- References may be requested closer to the offer stage rather than at the start of the process. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- The organisation has historically promoted from within for a majority of its senior and lead openings. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Compensation includes standard statutory benefits along with performance-linked variable pay.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
- In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
- Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.