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Shashank Bijarapu
Mid-level AI/ML & Data Engineer specializing in MLOps and cloud data pipelines
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AI/ML engineer (Merkle) with hands-on experience deploying RAG-based LLM applications and real-time recommendation engines into production. Strong in cloud/on-prem architectures, GPU autoscaling, caching, and network optimization—delivered measurable latency reductions (40–70%) and improved retrieval relevance by systematically benchmarking chunking/embedding configurations and validating pipelines via CI/CD.
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Key Strengths
- Production deployment of RAG-based LLM apps with retrieval quality and latency improvements
- Data-driven retrieval optimization (chunk size/overlap/embedding experiments using precision/recall and cosine similarity)
- Caching strategy implementation (in-memory + semantic) reducing vector DB load and cutting latency ~40%
- Cross-layer debugging across software, GPU hardware metrics, and network traces to isolate root causes
- Real-time inference optimization (batching, removing redundant computation) plus GPU autoscaling and traffic rerouting cutting latency ~70% and eliminating failed predictions
- Customer-specific Python customization with modular, config-driven design and unit tests for maintainability
- On-site client collaboration translating workflow observations into system changes that improved adoption
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