Job Description
Machine Learning is looking for an experienced Senior Machine Learning Solutions Architect to take ownership of the Machine Learning Engineering charter at a senior level. This is a role for someone who has already operated at scale and is comfortable setting direction rather than simply following one.
The successful candidate will bring approximately 14 years of progressively senior experience, holding Ph.D (ML/AI), with a demonstrated track record across Machine Learning Engineering, Statistical Modelling, Data Preprocessing, Python, TensorFlow/PyTorch. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Noida on a full-time arrangement. Compensation is benchmarked at INR 18.1-77.0 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within machine learning engineering.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
The package is structured with a fixed component, performance bonus, and standard health coverage. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Interested candidates should be prepared to walk through relevant past work during the interview process.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The organisation has historically promoted from within for a majority of its senior and lead openings. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The next logical step from this position is a senior or lead-track role within the same function.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
The successful candidate will bring approximately 14 years of progressively senior experience, holding Ph.D (ML/AI), with a demonstrated track record across Machine Learning Engineering, Statistical Modelling, Data Preprocessing, Python, TensorFlow/PyTorch. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Noida on a full-time arrangement. Compensation is benchmarked at INR 18.1-77.0 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within machine learning engineering.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
The package is structured with a fixed component, performance bonus, and standard health coverage. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Interested candidates should be prepared to walk through relevant past work during the interview process.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The organisation has historically promoted from within for a majority of its senior and lead openings. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The next logical step from this position is a senior or lead-track role within the same function.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.