Job Description
## AI Model Operations Associate
**Department:** Machine Learning Engineering | **Industry:** Machine Learning
**Location:** Hybrid - Bengaluru | **Job Type:** Full-time | **CTC:** INR 11.7-22.5 LPA
### Role Overview
This position calls for an AI Model Operations Associate who can take ownership of core responsibilities within our Machine Learning Engineering team. The role is positioned at the junior level and reports into the broader Machine Learning Engineering charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to machine learning engineering.
- Apply working knowledge of Deep Learning and Machine Learning Algorithms to daily execution.
- Coordinate with cross-functional stakeholders within Machine Learning.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- B.Tech (CS/AI) or equivalent qualification.
- 2 years of relevant experience (junior level).
- Working proficiency in: Deep Learning, Machine Learning Algorithms, Statistical Modelling, Python, Machine Learning Engineering, Cloud Platforms (AWS/GCP).
### Additional Notes
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Interested candidates should be prepared to walk through relevant past work during the interview process. Compensation includes standard statutory benefits along with performance-linked variable pay. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
**Department:** Machine Learning Engineering | **Industry:** Machine Learning
**Location:** Hybrid - Bengaluru | **Job Type:** Full-time | **CTC:** INR 11.7-22.5 LPA
### Role Overview
This position calls for an AI Model Operations Associate who can take ownership of core responsibilities within our Machine Learning Engineering team. The role is positioned at the junior level and reports into the broader Machine Learning Engineering charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to machine learning engineering.
- Apply working knowledge of Deep Learning and Machine Learning Algorithms to daily execution.
- Coordinate with cross-functional stakeholders within Machine Learning.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- B.Tech (CS/AI) or equivalent qualification.
- 2 years of relevant experience (junior level).
- Working proficiency in: Deep Learning, Machine Learning Algorithms, Statistical Modelling, Python, Machine Learning Engineering, Cloud Platforms (AWS/GCP).
### Additional Notes
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Interested candidates should be prepared to walk through relevant past work during the interview process. Compensation includes standard statutory benefits along with performance-linked variable pay. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.