Job Description
An opening exists for a Computer Vision Engineering Manager to join the Artificial Intelligence (AI) vertical, focused on Computer Vision. This is a senior level opening based out of Remote (Pan India), offered on a full-time basis.
The ideal candidate holds M.Tech (AI/ML) and brings roughly 15 years of hands-on exposure to computer vision. Day-to-day, the work draws heavily on Neural Network Architectures, TensorFlow/PyTorch, Model Deployment (MLOps), Computer Vision, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 39.6-105.7 LPA, in line with market standards for senior level roles within artificial intelligence (ai).
The organisation has historically promoted from within for a majority of its senior and lead openings. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Internal job postings are open to employees who've completed a minimum tenure in their current role. References may be requested closer to the offer stage rather than at the start of the process. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The package is structured with a fixed component, performance bonus, and standard health coverage.
Interested candidates should be prepared to walk through relevant past work during the interview process. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
The next logical step from this position is a senior or lead-track role within the same function. High performers in this role typically move into a broader ownership scope within 18-24 months. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
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. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Learning and certification support is available for tools and frameworks directly relevant to the role.
We value consistency and follow-through more than flashy credentials - if that matches how you work, apply in.
The ideal candidate holds M.Tech (AI/ML) and brings roughly 15 years of hands-on exposure to computer vision. Day-to-day, the work draws heavily on Neural Network Architectures, TensorFlow/PyTorch, Model Deployment (MLOps), Computer Vision, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 39.6-105.7 LPA, in line with market standards for senior level roles within artificial intelligence (ai).
The organisation has historically promoted from within for a majority of its senior and lead openings. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Internal job postings are open to employees who've completed a minimum tenure in their current role. References may be requested closer to the offer stage rather than at the start of the process. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The package is structured with a fixed component, performance bonus, and standard health coverage.
Interested candidates should be prepared to walk through relevant past work during the interview process. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
The next logical step from this position is a senior or lead-track role within the same function. High performers in this role typically move into a broader ownership scope within 18-24 months. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
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. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Learning and certification support is available for tools and frameworks directly relevant to the role.
We value consistency and follow-through more than flashy credentials - if that matches how you work, apply in.