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Yaswanth Thota Thota
Mid-level Data Analyst specializing in financial risk and healthcare analytics
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AI/ML engineer focused on real-time, production-grade LLM systems, with a robotics-adjacent mindset around latency/accuracy tradeoffs and modular pipelines. Built a scalable RAG-based assistant orchestrated as microservices on Kubernetes with Kafka async messaging, ONNX/quantization optimizations, and monitoring (Prometheus/Grafana), citing a ~35% hallucination reduction; has also experimented with ROS Noetic/Gazebo to understand ROS concepts.
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Key Strengths
- Designed and scaled a real-time LLM assistant using RAG + LangChain
- Reduced hallucinations by ~35% via real-time retrieval pipeline
- Production microservices deployment with Docker + Kubernetes (continuous training/model versioning, minimal downtime)
- Built low-latency inference interfaces via REST/gRPC with FastAPI
- Improved real-time throughput/latency using Kafka-based async decoupling
- Inference optimization using ONNX Runtime and quantization
- Reliability/observability for real-time systems with Prometheus + Grafana and drift monitoring
- Systems-level thinking mapping AI pipelines to ROS-style modular nodes and message passing
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