Job Description
## Senior Reinforcement Learning Engineer
**Department:** AI Research & Development | **Industry:** Artificial Intelligence (AI)
**Location:** Hybrid - Bengaluru | **Job Type:** Full-time | **CTC:** INR 13.7-65.5 LPA
### Role Overview
We are looking to onboard a Senior Reinforcement Learning Engineer to strengthen our AI Research & Development function within Artificial Intelligence (AI). The role is positioned at the senior level and reports into the broader AI Research & Development charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to ai research & development.
- Apply working knowledge of Deep Learning and Development 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
- Ph.D (AI) or equivalent qualification.
- 9 years of relevant experience (senior level).
- Working proficiency in: Deep Learning, Development, Model Deployment (MLOps), TensorFlow/PyTorch, Computer Vision, AI Research, Cloud Platforms (AWS/GCP/Azure), Python, Neural Network Architectures.
### Additional Notes
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Learning and certification support is available for tools and frameworks directly relevant to the role. 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 environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The next logical step from this position is a senior or lead-track role within the same function. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Compensation includes standard statutory benefits along with performance-linked variable pay. References may be requested closer to the offer stage rather than at the start of the process. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The package is structured with a fixed component, performance bonus, and standard health coverage. 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.
**Department:** AI Research & Development | **Industry:** Artificial Intelligence (AI)
**Location:** Hybrid - Bengaluru | **Job Type:** Full-time | **CTC:** INR 13.7-65.5 LPA
### Role Overview
We are looking to onboard a Senior Reinforcement Learning Engineer to strengthen our AI Research & Development function within Artificial Intelligence (AI). The role is positioned at the senior level and reports into the broader AI Research & Development charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to ai research & development.
- Apply working knowledge of Deep Learning and Development 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
- Ph.D (AI) or equivalent qualification.
- 9 years of relevant experience (senior level).
- Working proficiency in: Deep Learning, Development, Model Deployment (MLOps), TensorFlow/PyTorch, Computer Vision, AI Research, Cloud Platforms (AWS/GCP/Azure), Python, Neural Network Architectures.
### Additional Notes
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Learning and certification support is available for tools and frameworks directly relevant to the role. 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 environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The next logical step from this position is a senior or lead-track role within the same function. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Compensation includes standard statutory benefits along with performance-linked variable pay. References may be requested closer to the offer stage rather than at the start of the process. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The package is structured with a fixed component, performance bonus, and standard health coverage. 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.