Application Reliability Engineer – Data & AI
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The Data and AI Application Reliability Engineer you are responsible for owning the post-deployment health, latency, and performance of productionized AI application and Medallion architectures followed for applications.
Acting as the problem-solver for technical issues affecting data pipelines, databases, and deployed AI/ML models, ensuring continuous operation and high user satisfaction.
Partner with core Feature teams to perform root-cause analysis, optimize PySpark jobs, and reduce system debt
Build automated observability dashboards and self-healing mechanisms for automated failure recovery
As a part of Job you are required to balance your responsibilities between Proactive Engineering & Automation and Operational Reliability/ Production Health.
Key Responsibilities
Performance Optimization: Analyze and refactor resource-intensive PySpark jobs, queries, and API endpoints to optimize cost, execution speed, and compute efficiency.
Self-Healing Automation: Develop automated recovery routines, DAG rerun triggers,
and data quality checks to minimize manual intervention.
Engineering Alignment: Partner closely with Feature Teams and Architects to establish strict Definition of Done (DoD) standards and production readiness gates for current deployments.
Production Observability & Monitoring: Design, implement, and maintain real-time monitoring and alerting frameworks for Medallion architecture pipelines, feature stores, and AI Applications.
Incident Management & Root-Cause Analysis: Lead technical resolution for high-priority production incidents, conducting thorough post-mortems to eliminate recurring failure patterns.
Technical Expertise:
Azure Data Engineering Stack
Proficiency in Databricks , Python, and PySpark.
Azure (ADLS Gen2, Azure Data Factory, Key Vault, Azure DevOps)
Hands-on experience with Medallion architecture.
Cloud and DevOps Fundamentals
Understanding of cloud computing concepts and Se
📌 Application Reliability Engineer – Data (Pune)
🏢 Michelin
📍 Pune