Job Description
Machine Learning is looking for an experienced Deep Learning Systems Architect to take ownership of the Deep Learning 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 12 years of progressively senior experience, holding M.Tech (Machine Learning), with a demonstrated track record across Deep Learning, Model Evaluation & Tuning, Machine Learning Algorithms, Computer Vision/NLP Basics, Python. 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 Mumbai on a leadership - full-time arrangement. Compensation is benchmarked at INR 30.6-67.8 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 deep learning.
Learning and certification support is available for tools and frameworks directly relevant to the role. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The next logical step from this position is a senior or lead-track role within the same function.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview process.
References may be requested closer to the offer stage rather than at the start of the process. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
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. 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. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
The successful candidate will bring approximately 12 years of progressively senior experience, holding M.Tech (Machine Learning), with a demonstrated track record across Deep Learning, Model Evaluation & Tuning, Machine Learning Algorithms, Computer Vision/NLP Basics, Python. 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 Mumbai on a leadership - full-time arrangement. Compensation is benchmarked at INR 30.6-67.8 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 deep learning.
Learning and certification support is available for tools and frameworks directly relevant to the role. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The next logical step from this position is a senior or lead-track role within the same function.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview process.
References may be requested closer to the offer stage rather than at the start of the process. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
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. 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. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.