Saurabh Singh

Developed ingestion pipelines and AI-assisted workflows to process unstructured data and produce LLM-optimized outputs using LangGraph and LangChain. Led a cloud-native orchestration platform for automated lifecycle management (start/stop) of OCI resources across multiple regions using Python, Java, and React-based control interfaces. Drove architecture decisions for high availability, fault tolerance, and cost optimization for enterprise cloud customers, improving efficiency by 50%. Integrated RAG systems for context-aware recommendations and implemented infrastructure-as-code with Terraform and Dockerized services for maintainable deployments.

Collaborated with data science teams to design and implement scalable pipelines generating AI-driven infinite question sets using Python, Pandas, NumPy, and embeddings. Built backend services with Python and FastAPI to process and distribute dynamically generated content. Designed AWS-based data pipelines using S3, Lambda, and Redshift for high-volume ingestion. Improved deployment workflows and introduced MLOps practices for integrating models, working with frontend teams to integrate React-based dashboards for content management.

Worked on Tax Data Hub by architecting and building a centralized data warehouse consolidating tax data from multiple enterprise systems into BigQuery; designed Pandas and PySpark-based Python ETL pipelines to process large-scale financial datasets, improving reporting efficiency and data reliability across finance and compliance. Built a Validation Engine to compare relational SQL data with NoSQL documents (MongoDB, CosmosDB) using a high-performance Python validation service, and developed scalable FastAPI microservices with RESTful APIs plus automated CI/CD pipelines to ensure cross-database consistency and optimized query performance. Mentored junior developers and led backend implementation efforts.