AI Engineer, Machine Learning Engineer, and Data Scientist with a proven track record delivering production-grade systems for enterprise clients across the US, Canada, and Singapore. Specializes in Large Language Models …
AI Engineer, Machine Learning Engineer, and Data Scientist with a proven track record delivering production-grade systems for enterprise clients across the US, Canada, and Singapore. Specializes in Large Language Models (LLM), Retrieval-Augmented Generation (RAG), Applied AI, and Multi-Agent Orchestration — with deep expertise in Natural Language Processing (NLP), Deep Learning, and Computer Vision. Delivered 0.14 ms P95 API latency and 60% faster model deployment in production environments on GCP and AWS.
Architected the "Neural Concierge" Multi-Agent LLM Orchestration system on GCP using Docker, processing 290K+ records to deliver real-time AI-driven personalized matching via XGBoost (rank:pairwise) and Deep Learning
Engineered the "DNA Affinity" ML ranking engine (40% Loyalty / 25% Frequency / 20% Behavior / 15% Context), achieving 0.14 ms P95 API latency by implementing gRPC, Feast Feature Store, and Redis caching
Hardened platform security and ensured HIPAA-aligned healthtech compliance by implementing WireGuard tunneling, RS256 JWT authentication, and Tenant Isolation — fully documented via Swagger (OpenAPI)
Accelerated model time-to-production by 60% for 500+ users by deploying Deep Learning forecasting models on Kubernetes with robust Continuous Training (CT) pipelines
Optimized real-time inference to
Reduced inference latency by 40% for enterprise B2B clients by fine-tuning and deploying LLM chatbots using LangChain on AWS, applying Applied AI optimization techniques including quantization and prompt engineering
Eliminated 50% of manual recruitment screening and boosted hiring efficiency by 35% by designing a Multi-Agent automation architecture for end-to-end candidate evaluation
Reduced LLM hallucination rate by 35% and achieved 100% source-grounded responses by deploying FAISS Vector Database-backed RAG systems with Human-in-the-Loop (HITL) validation integrated into multi-LLM Llama-3 workflows
Enforced full HIPAA compliance across 1.5 TB of sensitive healthcare and medical imaging datasets by building end-to-end RAG and NLP pipelines with SHA-256 data anonymization protocols — leveraging Biomedical Engineering domain expertise
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