Imran Sarwar

Owned production AI/ML data platforms across architecture and operations, partnering with Product, ML, Data, and Frontend teams to define data models and APIs. Built Python/FastAPI services and RabbitMQ-based message contracts. Processed large document workflows with OCR and rule-based validation, storing historical and latest feature values in PostgreSQL and Redis for billing and consumption use cases. Implemented reusable, test-covered services with CI/CD and cloud-native deployments on Terraform and Kubernetes, using Claude Code for prototypes and generated tests with human review.

Designed data models and Airflow-orchestrated ETL pipelines for store, product, shipment, and delivery feeds. Transformed operational data into validated analytical datasets using Python, PySpark, SQL, and data processing tooling. Collaborated with Data Science on downstream requirements and ensured datasets matched analytical expectations. Supported production data readiness through modeling and repeatable pipeline development practices.

Designed and operated high-throughput, low-latency Kafka pipelines and sports-data services on AWS. Built and maintained AWS data flows using S3 and Glue, managing data in PostgreSQL and Redis. Deployed services on Kubernetes and used observability tools to diagnose production issues and improve reliability and performance. Worked on operational tuning and incident remediation to keep pipelines stable under load.

Developed Spark/Scala ETL pipelines on AWS and Hadoop/Hive to process large health-insurance datasets. Integrated predictive-model outputs into downstream analytical systems in a batch data flow. Implemented distributed transformations using SQL and Kafka-based ingestion or coordination. Worked on pipeline reliability and performance for data-intensive workloads.

Built AWS-hosted, event-driven Payment and Wallet services for a high-traffic platform using Kafka-based processing. Implemented correctness safeguards such as typed errors and idempotency and added automated testing and monitoring for reliable transaction handling. Used Redis for supporting state needs in the service workflows. Focused on maintaining transaction correctness while keeping the system responsive under load.

Maintained and enhanced UBS banking microservices and data integrations in a regulated environment. Implemented and delivered tested changes using Java, Spring Cloud, Kafka, and REST APIs. Worked with distributed coordination components to ensure correct behavior across services. Focused on integration stability and correctness for banking-related data flows.

Led a team of 6+ engineers building AWS-hosted services and owned overall architecture and delivery. Established engineering standards for clean code, testing, and code review, and mentored developers. Delivered backend services using Java, Spring Boot, Spring Cloud, RabbitMQ, and REST APIs. Ensured teams could ship reliably with consistent architecture and practices.

Architected and delivered a greenfield product spanning web, mobile, APIs, and backend microservices. Owned technical delivery end-to-end from design through production release. Implemented backend microservices using Java, Spring Boot, Spring Cloud, RabbitMQ, and REST APIs. Ensured the system design supported scalable integration across components.

Built scheduled ETL and alert services and supported monitoring and administration workflows for data systems. Developed data processing and integration capabilities using SQL, Cassandra, and Talend. Worked on service discovery and operational support components as part of the backend infrastructure. Focused on building dependable batch jobs and reliable data operations using established tooling.