Job Description
Position: AI Education Consultant
Function: AI Training & Upskilling Programs
Industry: Artificial Intelligence (AI)
Grade: mid level
Location: Pune
Employment Type: Full-time
Compensation: INR 4.0-15.5 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai training & upskilling programs, in accordance with the standards and processes laid down by Artificial Intelligence (AI).
Minimum Qualification: M.Tech (AI/ML)
Minimum Experience: 3 years
Core Competencies Required: AI Training, Upskilling Programs, Python, Data Preprocessing, Computer Vision, Model Deployment (MLOps), Cloud Platforms (AWS/GCP/Azure), Natural Language Processing
Further Information:
High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Learning and certification support is available for tools and frameworks directly relevant to the role. The organisation has historically promoted from within for a majority of its senior and lead openings. There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Compensation includes standard statutory benefits along with performance-linked variable pay. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
Function: AI Training & Upskilling Programs
Industry: Artificial Intelligence (AI)
Grade: mid level
Location: Pune
Employment Type: Full-time
Compensation: INR 4.0-15.5 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai training & upskilling programs, in accordance with the standards and processes laid down by Artificial Intelligence (AI).
Minimum Qualification: M.Tech (AI/ML)
Minimum Experience: 3 years
Core Competencies Required: AI Training, Upskilling Programs, Python, Data Preprocessing, Computer Vision, Model Deployment (MLOps), Cloud Platforms (AWS/GCP/Azure), Natural Language Processing
Further Information:
High performers in this role typically move into a broader ownership scope within 18-24 months. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Learning and certification support is available for tools and frameworks directly relevant to the role. The organisation has historically promoted from within for a majority of its senior and lead openings. There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Compensation includes standard statutory benefits along with performance-linked variable pay. 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. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.