Job Description
ML Deployment Associate | Kolkata | INR 9.0-15.6 LPA
Machine Learning Engineering role, junior level, full-time.
Need: B.Tech (CS/AI), 1+ yrs.
Core skills: Statistical Modelling, Data Preprocessing, Python, Machine Learning Engineering.
If this fits your profile in machine learning engineering, apply now.
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.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. References may be requested closer to the offer stage rather than at the start of the process. High performers in this role typically move into a broader ownership scope within 18-24 months.
Compensation includes standard statutory benefits along with performance-linked variable pay. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. 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.
Machine Learning Engineering role, junior level, full-time.
Need: B.Tech (CS/AI), 1+ yrs.
Core skills: Statistical Modelling, Data Preprocessing, Python, Machine Learning Engineering.
If this fits your profile in machine learning engineering, apply now.
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.
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. References may be requested closer to the offer stage rather than at the start of the process. High performers in this role typically move into a broader ownership scope within 18-24 months.
Compensation includes standard statutory benefits along with performance-linked variable pay. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. 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.