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Sreelekha Vuppala
Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms
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About
GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.
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Key Strengths
- Built and deployed production RAG system for compliance/ops Q&A over unstructured documents
- Improved retrieval quality via semantic chunking, metadata filtering, and continuous tuning
- Scaled high-precision retrieval using Pinecone vector DB
- Designed stateless FastAPI microservice on Kubernetes integrated with existing data lakes
- Implemented reliability practices: automated re-ingestion/re-embedding, monitoring (precision/latency/faithfulness), and low-confidence 'no answer' guardrails
- Orchestrated end-to-end AI/GenAI pipelines with Apache Airflow (ingestion, embeddings, evaluation, training)
- Handled compute-intensive embedding generation with batching, task parallelism, resource limits, monitoring/alerting, and idempotent reruns
- Designed AI agents/workflows with modular steps, guardrails, unit + scenario testing, KPIs, and human-in-the-loop for high-risk actions
- Delivered GenAI-powered incident triage with measurable impact (35% response time reduction, 40% fewer misclassified tickets)
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