Job Description
Machine Learning is looking for an experienced MLOps Performance Engineer to take ownership of the MLOps charter at a mid 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 5 years of progressively senior experience, holding B.Tech (CS/AI), with a demonstrated track record across Deep Learning, MLOps, Python, Computer Vision/NLP Basics, Cloud Platforms (AWS/GCP). 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 Hyderabad on a contract arrangement. Compensation is benchmarked at INR 13.4-30.7 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 mlops.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. References may be requested closer to the offer stage rather than at the start of the process.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. High performers in this role typically move into a broader ownership scope within 18-24 months.
Interested candidates should be prepared to walk through relevant past work during the interview process. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
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 hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
The successful candidate will bring approximately 5 years of progressively senior experience, holding B.Tech (CS/AI), with a demonstrated track record across Deep Learning, MLOps, Python, Computer Vision/NLP Basics, Cloud Platforms (AWS/GCP). 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 Hyderabad on a contract arrangement. Compensation is benchmarked at INR 13.4-30.7 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 mlops.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. References may be requested closer to the offer stage rather than at the start of the process.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Relocation assistance may be extended for candidates moving from a different city, subject to approval. High performers in this role typically move into a broader ownership scope within 18-24 months.
Interested candidates should be prepared to walk through relevant past work during the interview process. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
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 hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. There is a defined internal mobility path for people who want to specialise deeper or move into people management.