I build
AI Engineer with ~4 years of experience designing and deploying production-grade LLM systems, RAG pipelines, and multi-agent workflows. I specialize in building scalable AI systems on AWS — from data pipelines and retrieval systems to low-latency inference and LLM evaluation — turning AI into reliable, real-world applications.
I'm Yajurved Jayavarapu, an AI Engineer based in San Francisco, CA with a Master’s in Data Science from the University of Alabama at Birmingham. I specialize in Generative AI, building LLM systems, RAG pipelines, and agentic workflows that operate reliably in production environments.
My experience spans Syneos Health, UAB, and Thomson Reuters — where I designed and deployed scalable ML and NLP systems, built distributed data pipelines with Spark, and optimized workflows reducing latency by 35% and manual effort by 60%. I work extensively with AWS Bedrock, LangChain, LangGraph, vector databases, and cloud-native MLOps to deliver end-to-end AI systems.
From LLM orchestration frameworks to distributed data engineering — the full stack of modern AI.
Production-grade AI systems — from grounded RAG to multi-agent verification.
Built a grounded RAG system that enforces document-only answers and safe refusals to eliminate hallucinations. Features agent-based query routing, hybrid retrieval (BM25 + dense vectors), metadata-aware filtering, chunk-level traceability, and integration tests for end-to-end LLM validation.
End-to-end health data analytics pipeline ingesting Apple Watch metrics, applying time-series analysis and anomaly detection, and surfacing insights through an interactive analytics dashboard. Demonstrated 40% improvement in data reliability metrics.
Multi-agent RAG system with dedicated Planner, Retriever, Answer, and Verifier agents. Implements query decomposition, multi-hop retrieval, citation enforcement, and retry logic using modular state machines and structured JSON outputs.
Built Spark-based ML pipelines on Hadoop clusters for large-scale text classification and sentiment analysis at Thomson Reuters. Applied cross-validation and robust metrics (AUC, F1, Precision, Recall) with reproducible training and evaluation workflows.
Orchestrated automated ETL and ML workflows using Airflow DAGs on AWS at Syneos Health. Built reusable ML components consumed by multiple downstream analytics and AI workflows, reducing data processing latency by 35%.
Engineered real-time data ingestion pipelines from multiple sensor and API sources at UAB, improving data accuracy by 25%. Built interactive dashboards backed by standardized datasets, with anomaly detection and unified analytics layer for downstream modeling.
Whether you have a full-time AI/ML opportunity, a research collaboration, or want to discuss LLMs and RAG systems — my inbox is always open. Based in San Francisco, open to remote.