AI Ops Engineer - Enterprise Data Platform

• AI Application Development
• Data, Digital and Cloud acceleration using AI
• AI Native Product Engineering Location: India Locations (Hyderabad, Bangalore, Mumbai or Gurgaon) Level: Senior Associate / Manager Required Experience: 5 - 8 years

Role Summary We are looking for a AI Ops Engineer to be part of team managing the end-to-end health of a client's enterprise data estate, operating as the single accountable control tower across a hybrid platform. What distinguishes this role is how you get there: you will build a service that detects, correlates and increasingly resolves on its own, applying AIOps and GenAI to compress triage, predict failure on the critical path, and convert repetitive manual intervention into automation - while keeping human judgement at the decision gates that matter. The role requires the candidate to exhibit engagement and technical leadership in equal measure. You will be the face of the service to client IT and business leadership, run the governance cadence and hold the delivery relationship.

Responsibilities Act as the primary point of accountability for critical data objects and business domains as per assigned responsibilities Drive service scorecard initiatives: SLA attainment, MTTR, data freshness, repeat-failure rate, pass rate, connector stability and FinOps. Participate in client meetings during major incidents and own the post-incident review. Contribute towards the Standard Operating Procedure for the estate - scope, monitoring framework, severity matrix, RACI, escalation paths, notification matrix and closure criteria - and keep it current as the platform evolves. Drive triage activities for P1 and P2 incidents, assign the resolver domain, run the cross-party bridge Drive problem management - convert recurring incidents into permanent fixes, and burn down the problem backlog Maintain the technical standard for the service across legacy and modern technical stacks, mentor juniors in the team Follow the data architecture standards for the estate, govern the data model - dimensional design, conformed dimensions and business keys, SCD treatment, and semantic consistency Lead diagnosis on major incidents across domain boundaries - distinguishing an ETL fault from a database fault, a connector fault from a source schema change, a report failure from warehouse contention. Drive initiatives to establish AIOps or intelligent observability

Requirements Bachelor’s/Master’s degree in Computer Science, Engineering, or related field (or equivalent practical experience). 5-8 years in data engineering, data warehousing or data operations, with experience of working in managed service or Data Operations function. Minimum 3 years experience in implementing data pipelines using dbt Strong SQL and data modelling expertise, including dimensional modelling and warehouse performance tuning. Demonstrated experience working in high touch operations projects - running governance forums, owning SLAs and service reporting, and managing escalations with senior IT and business stakeholders. Practical experience with enterprise job scheduling (Tidal, Control-M, Autosys or equivalent) and with ITSM process in ServiceNow Excellent written and verbal communication - able to translate a technical fault into business impact for an executive audience. Preferred Experience in implementing or supporting Snowflake data platforms is preferred Experience in Informatica PowerCenter and a relational data warehouse platform will be added bonus. Working knowledge of MicroStrategy or a comparable enterprise BI platform. Familiarity with Python or PySpark for operational tooling and automation. Exposure to cloud cost management and FinOps practice, particularly Snowflake credit optimisation. ITIL certification or equivalent practical grounding in service management.