Job Description
Join us as a ML Automation Associate and help us push MLOps forward within Machine Learning.
You'll be working out of Chandigarh (contract), and honestly, the day-to-day is going to revolve around TensorFlow/PyTorch and MLOps. We're not looking for someone who just ticks boxes - we want someone who actually cares about mlops.
On paper, we'd expect M.Tech (Machine Learning) and somewhere around 2 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 12.8-21.3 LPA - fair for the junior level band and open to discussion based on what you bring.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. 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.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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. Interested candidates should be prepared to walk through relevant past work during the interview process.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. The organisation has historically promoted from within for a majority of its senior and lead openings.
You'll be working out of Chandigarh (contract), and honestly, the day-to-day is going to revolve around TensorFlow/PyTorch and MLOps. We're not looking for someone who just ticks boxes - we want someone who actually cares about mlops.
On paper, we'd expect M.Tech (Machine Learning) and somewhere around 2 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 12.8-21.3 LPA - fair for the junior level band and open to discussion based on what you bring.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. 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.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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. Interested candidates should be prepared to walk through relevant past work during the interview process.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. The organisation has historically promoted from within for a majority of its senior and lead openings.