Job Description
**AI Technical Specialist – Senior** | Hybrid - Bengaluru | Full-time | INR 26.8-68.2 LPA
**About the role:**
- Position within AI Research & Development, under the Artificial Intelligence (AI) vertical
- Level: senior level
- Reports to the functional head for AI Research & Development
**What you'll do:**
- Handle core ai research & development activities on a day-to-day basis
- Use Model Deployment (MLOps) regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Model Deployment (MLOps)
- Neural Network Architectures
- Deep Learning
- AI Research
- Development
- Statistical Modelling
- M.Tech (AI/ML)
- 9+ years of relevant experience
**Good to know:**
- A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
- Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The organisation has historically promoted from within for a majority of its senior and lead openings.
- In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- The next logical step from this position is a senior or lead-track role within the same function. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Internal job postings are open to employees who've completed a minimum tenure in their current role. 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. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
- There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
- Most days include at least one review checkpoint where progress is discussed against the plan for the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
- References may be requested closer to the offer stage rather than at the start of the process. Interested candidates should be prepared to walk through relevant past work during the interview process. 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.
**About the role:**
- Position within AI Research & Development, under the Artificial Intelligence (AI) vertical
- Level: senior level
- Reports to the functional head for AI Research & Development
**What you'll do:**
- Handle core ai research & development activities on a day-to-day basis
- Use Model Deployment (MLOps) regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Model Deployment (MLOps)
- Neural Network Architectures
- Deep Learning
- AI Research
- Development
- Statistical Modelling
- M.Tech (AI/ML)
- 9+ years of relevant experience
**Good to know:**
- A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
- Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The organisation has historically promoted from within for a majority of its senior and lead openings.
- In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- The next logical step from this position is a senior or lead-track role within the same function. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Internal job postings are open to employees who've completed a minimum tenure in their current role. 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. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
- There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
- Most days include at least one review checkpoint where progress is discussed against the plan for the week. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
- References may be requested closer to the offer stage rather than at the start of the process. Interested candidates should be prepared to walk through relevant past work during the interview process. 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.