SDE IV (ML Engineering)

InMobi Advertising is a global technology leader enabling marketers to win the moments that matter. Our platform reaches over 2 billion people in 150+ countries, delivering results with privacy-first principles. We combine lock screens, apps, TVs, and the open web with AI and machine learning to enable precise personalization and measurable impact. Our Glance AI initiative shapes AI-driven commerce with highly personalized shopping experiences across diverse categories. InMobi has received recognition as a Great Place to Work and by MIT Technology Review and Fast Company, with offices around the world.

A SDE IV in Data Platform Engineering at InMobi Advertising in Lucknow will help architect a self-serve Data Platform-as-a-Product that powers our global data ecosystem. You will integrate OSS tools, proprietary services, and Cloud/SaaS into unified infrastructure to provide production-grade data solutions for data engineers and analysts. This role requires deep data engineering expertise and platform engineering to deliver scalable, reliable data infrastructure.

Core Responsibilities

• Design and develop a cohesive data platform by bridging OSS tools (Spark, Flink, Airflow, Iceberg), internal services, and cloud offerings
• Build intuitive platform integrations that enable push-button data workflows
• Scale engineering efforts by operating distributed systems processing petabytes of data daily
• Manage multi-region Kubernetes infrastructure with elastic scalability and fault tolerance
• Optimize compute utilization for large-scale batch and real-time streaming (Spark/Flink clusters, Velox/Gluten acceleration) with sub-second latency
• Develop and maintain comprehensive observability, including telemetry (metrics, logs, traces) and data quality frameworks
• Enforce SLAs/SLOs with automated incident response and data validation

Requirements

• Deep data engineering expertise in batch and streaming pipelines, data modeling, query optimization, and governance
• Experience bridging OSS data tooling (e.g., Spark, Flink, Airflow, Iceberg) with internal services and cloud-based solutions
• Proven ability to design and implement production-grade data platforms for data engineers and analysts
• Experience scaling distributed systems, including multi-region Kubernetes infrastructure, with emphasis on reliability and fault tolerance
• Ability to optimize compute resources for large-scale data processing and real-time analytics, with a focus on low latency
• Strong emphasis on observability, data quality, and automated incident response to maintain 24/7 uptime