Job Description
Position: Data Platform Analytics Engineer
Function: Data Engineering
Industry: Machine Learning
Grade: mid level
Location: Bhubaneswar
Employment Type: Full-time
Compensation: INR 6.8-34.3 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with data engineering, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 5 years
Core Competencies Required: Computer Vision/NLP Basics, TensorFlow/PyTorch, Data Engineering, Model Evaluation & Tuning, Feature Engineering, Python, Machine Learning Algorithms, Statistical Modelling
Further Information:
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Learning and certification support is available for tools and frameworks directly relevant to the role. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. High performers in this role typically move into a broader ownership scope within 18-24 months. Internal job postings are open to employees who've completed a minimum tenure in their current role. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Function: Data Engineering
Industry: Machine Learning
Grade: mid level
Location: Bhubaneswar
Employment Type: Full-time
Compensation: INR 6.8-34.3 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with data engineering, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Tech (Machine Learning)
Minimum Experience: 5 years
Core Competencies Required: Computer Vision/NLP Basics, TensorFlow/PyTorch, Data Engineering, Model Evaluation & Tuning, Feature Engineering, Python, Machine Learning Algorithms, Statistical Modelling
Further Information:
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Learning and certification support is available for tools and frameworks directly relevant to the role. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. High performers in this role typically move into a broader ownership scope within 18-24 months. Internal job postings are open to employees who've completed a minimum tenure in their current role. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.