Jemi Jose Senior

Led architecture, design, and implementation of enterprise-scale AI, Generative AI, and Agentic AI solutions, building production-grade systems using RAG, transformer models, AI agents/bots, and cloud-native services. Built scalable RAG pipelines with semantic search, embeddings, chunking, metadata enrichment, and retrieval optimization to improve accuracy and relevance. Implemented end-to-end MLOps/LLMOps workflows with MLflow, Docker, Kubernetes, and CI/CD, including automated monitoring, drift detection, retraining/versioning, and explainability. Developed AI/ML APIs to integrate predictive and generative capabilities into enterprise applications, and delivered dashboards and reporting frameworks to communicate model outcomes and business impact. Established Responsible AI governance, evaluation/benchmarking, and compliance-minded controls (risk, fairness, explainability, auditability) for foundation model selection and deployment.

Designed and deployed end-to-end machine learning solutions on Azure using supervised/unsupervised learning, feature engineering, NLP, deep learning, and statistical modeling to produce scalable, production-ready AI systems. Built and optimized classification/regression/forecasting models (e.g., logistic regression, random forest, gradient boosting, XGBoost), and unsupervised solutions (K-means, DBSCAN, hierarchical clustering, dimensionality reduction) for segments and anomalies. Created robust Azure ML pipelines for ingestion, preprocessing, training, validation, deployment, and monitoring. Delivered NLP pipelines for preprocessing, embeddings, semantic similarity, retrieval, and transformer-based language understanding. Implemented MLOps best practices with MLflow, CI/CD, Docker, AKS-based orchestration, and explainability via SHAP/LIME, supporting reliable automated deployment and stakeholder-facing analytics.

Developed and deployed end-to-end ML pipelines for predictive analytics and classification using Python, scikit-learn, TensorFlow, and PyTorch, improving accuracy and business decisions. Built scalable feature engineering and preprocessing pipelines with PySpark/Pandas/NumPy, and delivered supervised/unsupervised models including regression, classification, clustering, and anomaly detection. Orchestrated training and deployment workflows using Azure Data Factory, Airflow, and Databricks; deployed models using Flask/FastAPI REST APIs, Docker, and Kubernetes for real-time inference. Implemented MLOps with MLflow, Git, and CI/CD including experiment tracking, model registry, automated deployment, and rollback strategies. Added monitoring and drift/performance evaluation, and ensured secure deployment using Key Vault and RBAC; integrated ML outputs with Power BI and supported advanced time-series solutions for forecasting and recommendations.