Job Description
An opening exists for a Machine Learning Integration Specialist to join the Machine Learning vertical, focused on Machine Learning Engineering. This is a mid level opening based out of Hybrid - Bengaluru, offered on a hybrid basis.
The ideal candidate holds B.Tech (CS/AI) and brings roughly 3 years of hands-on exposure to machine learning engineering. Day-to-day, the work draws heavily on Feature Engineering, Machine Learning Engineering, Machine Learning Algorithms, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 7.9-25.2 LPA, in line with market standards for mid level roles within machine learning.
Interested candidates should be prepared to walk through relevant past work during the interview process. References may be requested closer to the offer stage rather than at the start of the process.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Compensation includes standard statutory benefits along with performance-linked variable pay. The organisation has historically promoted from within for a majority of its senior and lead openings.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The package is structured with a fixed component, performance bonus, and standard health coverage. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
If your background aligns with the above and you're looking for your next step in Machine Learning Engineering, we'd like to hear from you.
The ideal candidate holds B.Tech (CS/AI) and brings roughly 3 years of hands-on exposure to machine learning engineering. Day-to-day, the work draws heavily on Feature Engineering, Machine Learning Engineering, Machine Learning Algorithms, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 7.9-25.2 LPA, in line with market standards for mid level roles within machine learning.
Interested candidates should be prepared to walk through relevant past work during the interview process. References may be requested closer to the offer stage rather than at the start of the process.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Compensation includes standard statutory benefits along with performance-linked variable pay. The organisation has historically promoted from within for a majority of its senior and lead openings.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The package is structured with a fixed component, performance bonus, and standard health coverage. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
If your background aligns with the above and you're looking for your next step in Machine Learning Engineering, we'd like to hear from you.