Job Description
Position: Generative AI Integration Engineer
Function: Generative AI
Industry: Machine Learning
Grade: mid level
Location: Nagpur
Employment Type: Full-time
Compensation: INR 20.9-51.3 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with generative ai, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Sc (Data Science)
Minimum Experience: 4 years
Core Competencies Required: Cloud Platforms (AWS/GCP), Feature Engineering, Python, Data Preprocessing, Generative AI, Model Evaluation & Tuning
Further Information:
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Relocation assistance may be extended for candidates moving from a different city, subject to approval. References may be requested closer to the offer stage rather than at the start of the process. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Learning and certification support is available for tools and frameworks directly relevant to the role. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The organisation has historically promoted from within for a majority of its senior and lead openings. Compensation includes standard statutory benefits along with performance-linked variable pay. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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. Interested candidates should be prepared to walk through relevant past work during the interview process.
Function: Generative AI
Industry: Machine Learning
Grade: mid level
Location: Nagpur
Employment Type: Full-time
Compensation: INR 20.9-51.3 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with generative ai, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Sc (Data Science)
Minimum Experience: 4 years
Core Competencies Required: Cloud Platforms (AWS/GCP), Feature Engineering, Python, Data Preprocessing, Generative AI, Model Evaluation & Tuning
Further Information:
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Relocation assistance may be extended for candidates moving from a different city, subject to approval. References may be requested closer to the offer stage rather than at the start of the process. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Learning and certification support is available for tools and frameworks directly relevant to the role. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The organisation has historically promoted from within for a majority of its senior and lead openings. Compensation includes standard statutory benefits along with performance-linked variable pay. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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. Interested candidates should be prepared to walk through relevant past work during the interview process.