Job Description
AI/ML Solutions Architect | Noida | INR 52.3-88.9 LPA
Machine Learning Engineering role, senior level, full-time.
Need: M.Tech (Machine Learning), 9+ yrs.
Core skills: Cloud Platforms (AWS/GCP), Python, Statistical Modelling, Machine Learning Engineering, Deep Learning.
If this fits your profile in machine learning engineering, apply now.
Learning and certification support is available for tools and frameworks directly relevant to the role. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The organisation has historically promoted from within for a majority of its senior and lead openings.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Compensation includes standard statutory benefits along with performance-linked variable pay.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. 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.
The next logical step from this position is a senior or lead-track role within the same function. References may be requested closer to the offer stage rather than at the start of the process.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. High performers in this role typically move into a broader ownership scope within 18-24 months.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The package is structured with a fixed component, performance bonus, and standard health coverage. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Machine Learning Engineering role, senior level, full-time.
Need: M.Tech (Machine Learning), 9+ yrs.
Core skills: Cloud Platforms (AWS/GCP), Python, Statistical Modelling, Machine Learning Engineering, Deep Learning.
If this fits your profile in machine learning engineering, apply now.
Learning and certification support is available for tools and frameworks directly relevant to the role. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The organisation has historically promoted from within for a majority of its senior and lead openings.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Compensation includes standard statutory benefits along with performance-linked variable pay.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. 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.
The next logical step from this position is a senior or lead-track role within the same function. References may be requested closer to the offer stage rather than at the start of the process.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. High performers in this role typically move into a broader ownership scope within 18-24 months.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The package is structured with a fixed component, performance bonus, and standard health coverage. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.