Job Description
Position: Machine Learning Software Engineer
Function: Machine Learning Engineering
Industry: Application Development / Software Applications
Grade: mid level
Location: Hybrid - Bengaluru
Employment Type: Full-time
Compensation: INR 18.4-31.5 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with machine learning engineering, in accordance with the standards and processes laid down by Application Development / Software Applications.
Minimum Qualification: MCA
Minimum Experience: 6 years
Core Competencies Required: Debugging & Troubleshooting, Unit Testing, Java/Python/JavaScript, Machine Learning Engineering, Object-Oriented Design
Further Information:
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. 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. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Compensation includes standard statutory benefits along with performance-linked variable pay. High performers in this role typically move into a broader ownership scope within 18-24 months. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Learning and certification support is available for tools and frameworks directly relevant to the role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. References may be requested closer to the offer stage rather than at the start of the process. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
Function: Machine Learning Engineering
Industry: Application Development / Software Applications
Grade: mid level
Location: Hybrid - Bengaluru
Employment Type: Full-time
Compensation: INR 18.4-31.5 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with machine learning engineering, in accordance with the standards and processes laid down by Application Development / Software Applications.
Minimum Qualification: MCA
Minimum Experience: 6 years
Core Competencies Required: Debugging & Troubleshooting, Unit Testing, Java/Python/JavaScript, Machine Learning Engineering, Object-Oriented Design
Further Information:
The role alternates between independent deep work and short coordination huddles with stakeholders across functions. 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. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. 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. Compensation includes standard statutory benefits along with performance-linked variable pay. High performers in this role typically move into a broader ownership scope within 18-24 months. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Learning and certification support is available for tools and frameworks directly relevant to the role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. References may be requested closer to the offer stage rather than at the start of the process. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Relocation assistance may be extended for candidates moving from a different city, subject to approval.