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Harshavardhan Garikala
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
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About
Red Hat ML/LLM engineer who designed and deployed a production LLM-powered customer support automation system using RAG, improving latency by 30% via PEFT and vector search optimization. Built security and governance into retrieval (access-level filtering, encrypted Pinecone/ChromaDB) and delivered SHAP-based explainability via a dashboard for non-technical stakeholders. Experienced orchestrating distributed ML/RAG pipelines across AWS SageMaker and OpenShift with Airflow/Prefect, plus multi-agent workflows using CrewAI and LangGraph.
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Key Strengths
- Reduced vector search response time by 30% without degrading contextual relevance
- Production deployment of LLM/RAG system automating customer service responses
- Strong security-by-design for RAG (access-level filtering + encrypted vector stores)
- Explainability implementation using SHAP with stakeholder-facing dashboard
- Orchestrated 15+ ML workflows using Airflow + MLflow
- Scalable distributed pipeline design across AWS SageMaker and OpenShift using containerized tasks
- Reliability engineering in orchestration (retries, SLAs, health checks, monitoring)
- Multi-agent workflow design with clear roles and measurable success metrics
- End-to-end evaluation approach (unit/integration tests, offline benchmarks, online A/B tests)
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