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Bhargavi Karuku
Mid-level AI Engineer specializing in ML, NLP, and Generative AI
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About
AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.
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Key Strengths
- Built and deployed a production LLM/RAG investment recommendation system (Docker + CI/CD on cloud)
- Reduced inference latency to <200ms and lowered cloud costs via TensorRT INT8/INT4 quantization (2–4x GPU speedup; 50–75% memory reduction)
- Cut fine-tuning/training cost ~80% using QLoRA adapters while improving domain accuracy
- Strong orchestration experience: Airflow for daily ETL + scoring pipelines and Kubernetes for scaling LLM services
- Improved pipeline reliability with Airflow retries and Slack alerting to prevent delays in A/B testing and keep recommendations fresh
- Structured agent/workflow evaluation: unit/integration tests, synthetic data, A/B experiments, red teaming, and production monitoring with MLflow
- Effective cross-functional collaboration: translated RAG/LLM concepts into demos and Tableau metrics dashboards; added explainability and compliance checks based on PM feedback
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