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TEJASWI ARAVELLI
Junior Machine Learning Engineer specializing in Generative AI and analytics automation
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About
AI/LLM engineer who built a production intelligent support system using RAG over a vectorized documentation library, addressing real-world issues like lost-in-the-middle context failures and doc freshness via automated GitHub-driven re-embedding pipelines. Emphasizes rigorous agent evaluation (component/E2E/ops) and prefers lightweight, decoupled workflow automation using message brokers (Redis/RabbitMQ) over heavyweight orchestration frameworks.
Experience
Education
Key Strengths
- Built and deployed a production RAG-based intelligent support system to reduce support ticket burden
- Solved lost-in-the-middle issues via sliding-window chunking with overlap
- Maintained retrieval freshness with automated GitHub doc change detection and selective re-embedding/upserts
- Designs workflow automation using decoupled producer-consumer patterns with Redis/RabbitMQ instead of heavy orchestration frameworks
- Structured AI agent evaluation across component, end-to-end, and operational layers (including LLM-as-judge and red teaming)
- Model/retrieval/prompt selection framework balancing accuracy, latency, and cost
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