Job Description
We're hiring a Machine Learning Optimization Engineer to join our growing Machine Learning Engineering team - if this sounds like your kind of work, read on.
You'll be working out of Coimbatore (full-time), and honestly, the day-to-day is going to revolve around MLOps and Python. We're not looking for someone who just ticks boxes - we want someone who actually cares about machine learning engineering.
On paper, we'd expect M.Sc (Data Science) and somewhere around 4 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 10.3-41.6 LPA - fair for the mid level band and open to discussion based on what you bring.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Learning and certification support is available for tools and frameworks directly relevant to the role. References may be requested closer to the offer stage rather than at the start of the process.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The package is structured with a fixed component, performance bonus, and standard health coverage.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Interested candidates should be prepared to walk through relevant past work during the interview process. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Internal job postings are open to employees who've completed a minimum tenure in their current role.
You'll be working out of Coimbatore (full-time), and honestly, the day-to-day is going to revolve around MLOps and Python. We're not looking for someone who just ticks boxes - we want someone who actually cares about machine learning engineering.
On paper, we'd expect M.Sc (Data Science) and somewhere around 4 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 10.3-41.6 LPA - fair for the mid level band and open to discussion based on what you bring.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Learning and certification support is available for tools and frameworks directly relevant to the role. References may be requested closer to the offer stage rather than at the start of the process.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. The package is structured with a fixed component, performance bonus, and standard health coverage.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Interested candidates should be prepared to walk through relevant past work during the interview process. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Internal job postings are open to employees who've completed a minimum tenure in their current role.