M. Ethan

Directed the design and implementation of enterprise-scale AI/ML platforms, engineering end-to-end machine learning pipelines (data ingestion/transformation, distributed training, experimentation, validation, model registry, deployment, online inference, and performance monitoring) and developing generative AI solutions using foundation models/LLMs with prompt optimization, embeddings, vector search, RAG, hybrid retrieval, semantic ranking, and systematic LLM benchmarking. Built intelligent agentic systems integrating LLM-based planning, tool/function calling, multi-agent collaboration, workflow orchestration, context/memory management, external API orchestration, structured outputs, guardrails, and human approval steps. Implemented mature MLOps/LLMOps workflows covering experiment tracking, data/model/prompt lifecycle management, dataset versioning/lineage, reproducible training, automated testing, CI/CD, continuous evaluation, canary releases, and safe rollback procedures. Designed r

Developed and operationalized machine learning and deep learning models using Python, PyTorch, TensorFlow, scikit-learn, NumPy, pandas, and distributed computing technologies for data-driven applications and production workloads. Built NLP and generative AI solutions including LLM-powered assistants, conversational interfaces, document understanding, semantic search engines, automated summarization, entity extraction, and RAG applications. Optimized LLM pipelines through prompt design, embedding generation, vector indexing, hybrid search, semantic ranking and re-ranking, context window management, and retrieval benchmarking to improve answer relevance and factual consistency. Implemented AI-driven engagement and workflow systems for lead generation, prospect scoring, qualification, personalized follow-ups, scheduling, tool execution, and human escalation, integrating external APIs, CRM systems, and automation services. Built computer vision solutions for classification, object localiza

Designed and implemented end-to-end machine learning workflows using Python, SQL, pandas, NumPy, and scikit-learn, covering data acquisition, cleaning, transformation, feature construction, model development, cross-validation, performance assessment, and production integration. Trained, evaluated, and fine-tuned ML/DL models with PyTorch, TensorFlow, and scikit-learn for classification, regression, forecasting, anomaly identification, and complex pattern analysis. Built NLP pipelines for text cleaning, tokenization, vector embeddings, semantic matching, document classification, entity and information extraction, and conversational AI applications. Implemented computer vision solutions for image transformation, object localization/tracking, visual categorization, semantic segmentation, feature representation learning, and deep learning-based prediction/inference. Packaged and deployed ML applications using Docker, Git-based version control, CI/CD automation, and cloud computing services