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SUJAY Kanakamedala
Mid-level AI Developer & Machine Learning Engineer specializing in LLM and MLOps systems
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Built and deployed an enterprise RAG application at Centene to help clinical teams retrieve insights from large internal policy document sets, cutting manual research by 30–40%. Implemented custom domain-adapted embeddings (SageMaker + BERT transfer learning) and hybrid retrieval (BM25 + Pinecone) to drive a 22% relevance lift, and ran the system in production on AWS EKS with CI/CD, MLflow, and Prometheus monitoring (99% uptime, ~40% latency reduction).
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Key Strengths
- Architected and deployed enterprise RAG system for clinical policy retrieval
- Reduced manual research effort by 30–40% across business units
- Improved retrieval relevance by 22% using custom embeddings and hybrid retrieval (validated via BLEU and human review)
- Mitigated hallucinations and improved domain-specific accuracy for clinical jargon
- Production-grade deployment on AWS EKS with autoscaling, rolling updates, and 99% uptime
- Achieved ~40% latency reduction through containerized FastAPI services and orchestration
- Strong MLOps discipline: MLflow experiment tracking/model versioning, CI/CD, retraining pipelines
- Comprehensive testing strategy: unit/integration tests (Pytest) and load testing (Locust) with monitoring (Prometheus)
- Effective model/retrieval/prompt selection via benchmarking (GPT vs fine-tuned Llama 3), RAGAS scoring, and A/B tests
- Proven stakeholder collaboration with non-technical clinical policy teams via workshops and iterative demos (Streamlit)
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