Sayed Ebady

Architected an enterprise Generative AI and RAG platform using Python, LangChain, LLM APIs, vector search, and cloud-native services for semantic retrieval and conversational access across technical and operational knowledge sources. Designed production retrieval pipelines combining dense embeddings, metadata filtering, hybrid search, and re-ranking to improve relevance and reduce hallucinations. Built agentic workflows using LangGraph, tool calling, structured outputs, and retrieval orchestration to automate investigation of telemetry and distributed-system events. Developed scalable LLM inference APIs with Python, FastAPI, Docker, Kubernetes, Redis, and asynchronous processing. Implemented LLMOps evaluation and observability (retrieval quality, hallucination rates, prompt performance, latency, token usage) and established reusable prompt templates, guardrails, and CI/CD controls to reduce regressions.

Engineered production machine-learning pipelines using Python, PySpark, scikit-learn, Azure Databricks, MLflow, and Delta Lake for clinical, laboratory, manufacturing, and operational analytics. Developed classification and anomaly-detection models to identify high-risk events and abnormal patterns earlier. Built reusable feature-engineering pipelines for large-scale datasets using Spark and Delta Lake. Implemented MLflow experiment tracking, model registry, validation gates, and controlled deployments to ensure reproducibility and traceability. Developed NLP pipelines with transformer-based models for extracting entities and structured signals from scientific and clinical documents. Built real-time inference and event-processing workflows with Spark Structured Streaming and Azure Event Hubs.

Built predictive analytics pipelines using Python, SQL, scikit-learn, and AWS for enterprise customer, financial, and operational use cases. Developed supervised-learning models for classification, propensity scoring, forecasting, and risk-oriented analytics. Engineered reusable Python and REST API services for batch and near-real-time model inference so downstream applications could consume scores and recommendations. Designed feature-engineering and data-preparation pipelines across relational, document, and cloud data sources with automated schema validation and data-quality controls. Optimized transformation workloads with incremental processing, partitioning, and distributed-compute techniques. Introduced monitoring and validation checks for prediction distributions, feature consistency, and production failures.