Job Description
AI Model Training Associate | Jaipur | INR 9.6-25.8 LPA
Deep Learning role, junior level, full-time.
Need: B.Tech (CS/AI), 1+ yrs.
Core skills: Machine Learning Algorithms, Deep Learning, TensorFlow/PyTorch, Statistical Modelling, Python.
If this fits your profile in deep learning, apply now.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. 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 next logical step from this position is a senior or lead-track role within the same function.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
Deep Learning role, junior level, full-time.
Need: B.Tech (CS/AI), 1+ yrs.
Core skills: Machine Learning Algorithms, Deep Learning, TensorFlow/PyTorch, Statistical Modelling, Python.
If this fits your profile in deep learning, apply now.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. 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 next logical step from this position is a senior or lead-track role within the same function.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.