Job Description
## AI Model Optimization Engineer
**Department:** Deep Learning | **Industry:** Artificial Intelligence (AI)
**Location:** Hyderabad | **Job Type:** Hybrid | **CTC:** INR 13.7-40.0 LPA
### Role Overview
This position calls for an AI Model Optimization Engineer who can take ownership of core responsibilities within our Deep Learning team. The role is positioned at the mid level and reports into the broader Deep Learning charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to deep learning.
- Apply working knowledge of Model Deployment (MLOps) and Python to daily execution.
- Coordinate with cross-functional stakeholders within Artificial Intelligence (AI).
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Tech (AI/ML) or equivalent qualification.
- 7 years of relevant experience (mid level).
- Working proficiency in: Model Deployment (MLOps), Python, TensorFlow/PyTorch, Cloud Platforms (AWS/GCP/Azure), Neural Network Architectures, Deep Learning, Computer Vision.
### Additional Notes
Learning and certification support is available for tools and frameworks directly relevant to the role. 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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Most days include at least one review checkpoint where progress is discussed against the plan for the week. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Interested candidates should be prepared to walk through relevant past work during the interview process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The next logical step from this position is a senior or lead-track role within the same function. 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. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. High performers in this role typically move into a broader ownership scope within 18-24 months. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The organisation has historically promoted from within for a majority of its senior and lead openings. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. 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. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
**Department:** Deep Learning | **Industry:** Artificial Intelligence (AI)
**Location:** Hyderabad | **Job Type:** Hybrid | **CTC:** INR 13.7-40.0 LPA
### Role Overview
This position calls for an AI Model Optimization Engineer who can take ownership of core responsibilities within our Deep Learning team. The role is positioned at the mid level and reports into the broader Deep Learning charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to deep learning.
- Apply working knowledge of Model Deployment (MLOps) and Python to daily execution.
- Coordinate with cross-functional stakeholders within Artificial Intelligence (AI).
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Tech (AI/ML) or equivalent qualification.
- 7 years of relevant experience (mid level).
- Working proficiency in: Model Deployment (MLOps), Python, TensorFlow/PyTorch, Cloud Platforms (AWS/GCP/Azure), Neural Network Architectures, Deep Learning, Computer Vision.
### Additional Notes
Learning and certification support is available for tools and frameworks directly relevant to the role. 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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Most days include at least one review checkpoint where progress is discussed against the plan for the week. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Interested candidates should be prepared to walk through relevant past work during the interview process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The next logical step from this position is a senior or lead-track role within the same function. 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. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. High performers in this role typically move into a broader ownership scope within 18-24 months. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The organisation has historically promoted from within for a majority of its senior and lead openings. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. 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. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There is a defined internal mobility path for people who want to specialise deeper or move into people management.