Job Description
## Machine Learning Research Engineer – Generative AI
**Department:** Generative AI | **Industry:** Artificial Intelligence (AI)
**Location:** Hybrid - Pune | **Job Type:** Hybrid | **CTC:** INR 24.3-42.5 LPA
### Role Overview
The organisation is seeking a qualified Machine Learning Research Engineer – Generative AI to support ongoing initiatives in Generative AI. The role is positioned at the mid level and reports into the broader Generative AI charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to generative ai.
- Apply working knowledge of Machine Learning and TensorFlow/PyTorch 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.Sc (Data Science) or equivalent qualification.
- 6 years of relevant experience (mid level).
- Working proficiency in: Machine Learning, TensorFlow/PyTorch, Python, Data Preprocessing, Generative AI, Statistical Modelling.
### Additional Notes
References may be requested closer to the offer stage rather than at the start of the process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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. Interested candidates should be prepared to walk through relevant past work during the interview process. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The next logical step from this position is a senior or lead-track role within the same function. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
**Department:** Generative AI | **Industry:** Artificial Intelligence (AI)
**Location:** Hybrid - Pune | **Job Type:** Hybrid | **CTC:** INR 24.3-42.5 LPA
### Role Overview
The organisation is seeking a qualified Machine Learning Research Engineer – Generative AI to support ongoing initiatives in Generative AI. The role is positioned at the mid level and reports into the broader Generative AI charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to generative ai.
- Apply working knowledge of Machine Learning and TensorFlow/PyTorch 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.Sc (Data Science) or equivalent qualification.
- 6 years of relevant experience (mid level).
- Working proficiency in: Machine Learning, TensorFlow/PyTorch, Python, Data Preprocessing, Generative AI, Statistical Modelling.
### Additional Notes
References may be requested closer to the offer stage rather than at the start of the process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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. Interested candidates should be prepared to walk through relevant past work during the interview process. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The next logical step from this position is a senior or lead-track role within the same function. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.