Job Description
AI/ML Analyst | Chennai | INR 5.8-12.8 LPA
Machine Learning Engineering role, junior level, full-time (wfh).
Need: B.Tech/B.E, 2+ yrs.
Core skills: Data Cleaning & ETL, Machine Learning Engineering, Power BI/Tableau, Statistical Modelling, SQL.
If this fits your profile in machine learning engineering, apply now.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. 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. Learning and certification support is available for tools and frameworks directly relevant to the role. The next logical step from this position is a senior or lead-track role within the same function.
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.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. High performers in this role typically move into a broader ownership scope within 18-24 months.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. References may be requested closer to the offer stage rather than at the start of the process.
Machine Learning Engineering role, junior level, full-time (wfh).
Need: B.Tech/B.E, 2+ yrs.
Core skills: Data Cleaning & ETL, Machine Learning Engineering, Power BI/Tableau, Statistical Modelling, SQL.
If this fits your profile in machine learning engineering, apply now.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. 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. Learning and certification support is available for tools and frameworks directly relevant to the role. The next logical step from this position is a senior or lead-track role within the same function.
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.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. High performers in this role typically move into a broader ownership scope within 18-24 months.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. References may be requested closer to the offer stage rather than at the start of the process.