Job Description
## Machine Learning Data Engineer
**Department:** Machine Learning Engineering | **Industry:** Machine Learning
**Location:** Kolkata | **Job Type:** Full-time | **CTC:** INR 13.9-32.0 LPA
### Role Overview
An opening exists for a Machine Learning Data Engineer to join the Machine Learning vertical, focused on Machine Learning Engineering. The role is positioned at the mid 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 Machine Learning Algorithms and Machine Learning Engineering 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
- M.Tech (Machine Learning) or equivalent qualification.
- 3 years of relevant experience (mid level).
- Working proficiency in: Machine Learning Algorithms, Machine Learning Engineering, Computer Vision/NLP Basics, Feature Engineering, MLOps, TensorFlow/PyTorch, Python, Statistical Modelling.
### Additional Notes
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. High performers in this role typically move into a broader ownership scope within 18-24 months. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. 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. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. 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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Learning and certification support is available for tools and frameworks directly relevant to the role. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. References may be requested closer to the offer stage rather than at the start of the process.
**Department:** Machine Learning Engineering | **Industry:** Machine Learning
**Location:** Kolkata | **Job Type:** Full-time | **CTC:** INR 13.9-32.0 LPA
### Role Overview
An opening exists for a Machine Learning Data Engineer to join the Machine Learning vertical, focused on Machine Learning Engineering. The role is positioned at the mid 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 Machine Learning Algorithms and Machine Learning Engineering 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
- M.Tech (Machine Learning) or equivalent qualification.
- 3 years of relevant experience (mid level).
- Working proficiency in: Machine Learning Algorithms, Machine Learning Engineering, Computer Vision/NLP Basics, Feature Engineering, MLOps, TensorFlow/PyTorch, Python, Statistical Modelling.
### Additional Notes
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. High performers in this role typically move into a broader ownership scope within 18-24 months. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. 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. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. 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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Learning and certification support is available for tools and frameworks directly relevant to the role. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. References may be requested closer to the offer stage rather than at the start of the process.