Job Description
**Director of Deep Learning Engineering** | Ahmedabad | Hybrid | INR 14.5-52.3 LPA
**About the role:**
- Position within Deep Learning, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for Deep Learning
**What you'll do:**
- Handle core deep learning activities on a day-to-day basis
- Use TensorFlow/PyTorch 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:**
- TensorFlow/PyTorch
- Deep Learning
- Machine Learning Algorithms
- Statistical Modelling
- Python
- Data Preprocessing
- Model Evaluation & Tuning
- MLOps
- M.Sc (Data Science)
- 15+ years of relevant experience
**Good to know:**
- The package is structured with a fixed component, performance bonus, and standard health coverage. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- High performers in this role typically move into a broader ownership scope within 18-24 months. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Compensation includes standard statutory benefits along with performance-linked variable pay.
- 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. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
- Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The organisation has historically promoted from within for a majority of its senior and lead openings.
- There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
- Learning and certification support is available for tools and frameworks directly relevant to the role. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- 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 role alternates between independent deep work and short coordination huddles with stakeholders across functions. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
**About the role:**
- Position within Deep Learning, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for Deep Learning
**What you'll do:**
- Handle core deep learning activities on a day-to-day basis
- Use TensorFlow/PyTorch 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:**
- TensorFlow/PyTorch
- Deep Learning
- Machine Learning Algorithms
- Statistical Modelling
- Python
- Data Preprocessing
- Model Evaluation & Tuning
- MLOps
- M.Sc (Data Science)
- 15+ years of relevant experience
**Good to know:**
- The package is structured with a fixed component, performance bonus, and standard health coverage. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- High performers in this role typically move into a broader ownership scope within 18-24 months. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Compensation includes standard statutory benefits along with performance-linked variable pay.
- 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. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
- Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The organisation has historically promoted from within for a majority of its senior and lead openings.
- There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
- Learning and certification support is available for tools and frameworks directly relevant to the role. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
- 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 role alternates between independent deep work and short coordination huddles with stakeholders across functions. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.