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Krishna vamsi Dhulipalla
Mid-level Software & ML Engineer specializing in agentic LLM systems and ML infrastructure
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About
Built and deployed an LLM-to-SQL automation system in a closed/internal environment, using a retriever–reranker–validator architecture on Kubernetes with strong security controls (semantic + rule-based validation and RBAC), achieving 99% uptime and cutting manual query time ~40%. Also worked on genomic sequence classification and semantic search workflows, orchestrating data prep with Airflow, tracking/deploying with MLflow, and optimizing distributed multi-GPU training on a university Kubernetes cluster.
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Key Strengths
- Built and deployed production LLM system for automated SQL lookups on Kubernetes (99% uptime)
- Reduced manual query/reporting time by ~40–50% through LLM automation
- Strong focus on secure LLM outputs via semantic + rule-based SQL validation and RBAC
- End-to-end MLOps/orchestration experience (Airflow, MLflow, Kubernetes, Docker)
- Experience optimizing distributed multi-GPU training workloads on shared clusters
- Rigorous agent/workflow evaluation approach (modular design, unit/integration/E2E tests, A/B testing, production monitoring with Grafana/Prometheus)
- Effective collaboration with non-technical stakeholders (finance) by adding human-in-the-loop SQL approval to build trust
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2 publications
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