Sagar Admane
I’m Sagar Admane, an AI Engineer focused on building production-grade LLM systems, GraphRAG pipelines, and agentic workflows.
About
I’m Sagar Admane, an AI Engineer focused on building production-grade LLM systems, GraphRAG pipelines, and agentic workflows. I enjoy turning messy engineering knowledge—like repositories, pull requests, and reviews—into reliable backends that can answer architecture, ownership, and dependency questions.
Across my experience, I’ve integrated LLMs with backend infrastructure and event-driven systems using Python/FastAPI, Django, Kafka/CDC, Neo4j, and Qdrant, and I’ve shipped developer-focused automation such as an agentic GitHub workflow assistant. I’m especially interested in reliable orchestration, structured tool-calling outputs, and building systems that are observable and dependable in real production environments.
- 1 year's experience
- Joined October 2026
Industry experience
- Software & Internet
- Professional Services
- Computers & Electronics
More experts in Hyderabad, India
Work experience· 4 roles
Built Django backend services supporting production business workflows and partner-facing applications. Developed conversational AI workflow APIs that collect customer requirements and power recommendation pipelines. Integrated Django and LoopBack services to improve communication and reduce API latency. Improved API reliability by adding validation, stronger error handling, and production-ready backend workflow logic.
Implemented a Spring Boot microservice with REST endpoints including retry logic and request validation, reducing integration defects. Designed a Kafka + CDC event pipeline across 3+ microservices with ordering and exactly-once processing for transaction consistency. Resolved production incidents related to Kafka offset management by adding transactional commit handling, reducing data loss significantly.
Built Spring Boot microservices with REST APIs, request validation, and retry mechanisms for internal integrations. Worked on Kafka-based event pipelines using CDC to synchronize data across multiple microservices. Investigated and resolved production issues related to Kafka offsets and transactional messaging, reducing data loss during failures. Improved distributed service reliability with better error handling and transactional processing.
Education
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