Production AI/ML and Generative AI Engineer

Michael Aaron Senior AI/ML Engineer

MICHAEL AARON

Senior AI/ML Engineer Generative AI Engineer Machine Learning Engineer Plano, Texas 737-***-**** *******.*****.***@*****.*** PROFESSIONAL SUMMARY

Senior AI/ML Engineer with 8+ years of experience building and deploying production AI/ML systems across healthcare, finance, retail, SaaS, and enterprise environments. Hands-on expertise in Generative AI, LLMs, RAG, agentic workflows, NLP, predictive modeling, and MLOps. Strong in Python, PyTorch, TensorFlow, LangChain/LangGraph, Hugging Face, and AWS/Azure/GCP. Known for taking ambiguous business problems from prototype to reliable production systems, improving automation, model quality, deployment speed, and operational reliability. Technical SKILLS

AI/ML: Python, Machine Learning, Deep Learning, Predictive Modeling, Classification, Regression, Forecasting, Anomaly Detection, NLP, Computer Vision (CNNs, OpenCV), Hyperparameter Tuning Generative AI: LLMs, RAG, AI Agents, Agentic/Multi-Agent Workflows, Prompt Engineering, Fine-Tuning, Embeddings, Semantic Search, Retrieval Reranking, LLM Evaluation, LangChain, LangGraph ML Engineering: PyTorch, TensorFlow, Scikit-learn, XGBoost, Hugging Face Transformers, BERT, Model Optimization, Feature Engineering, MLflow, Airflow, Model Monitoring Data & Cloud: SQL, Spark/PySpark, Databricks, Snowflake, AWS, Azure, GCP, Vertex AI, Azure OpenAI, SageMaker, Bedrock, Tableau, Power BI

Production: Docker, Kubernetes, Terraform, CI/CD, GitHub Actions, FastAPI, REST APIs, PostgreSQL, MongoDB, Redis, Elasticsearch, Vector Databases (Pinecone, FAISS), IAM/RBAC, Encryption PROFESSIONAL EXPERIENCE

Senior AI/ML Engineer Jul 2023 - Present

Zero2AI - Applied AI / GenAI consulting across enterprise, life sciences and health-tech use cases

Designed and deployed enterprise RAG and agentic AI workflows with Azure OpenAI, OpenAI, LangChain/LangGraph and vector databases, cutting knowledge-discovery and document-review effort by 37% in targeted workflows.

Led technical design and delivery for 2-3 concurrent GenAI engagements, setting architecture direction and reviewing implementation approach for a small team of contributing engineers.

Identified and remediated critical security gaps (weak API auth, over-permissive data access) in production GenAI/RAG workflows by implementing OAuth2/JWT authentication and RBAC controls, reducing unauthorized- access incidents by 40% and bringing workflows into HIPAA-aligned compliance.

Mentored junior and mid-level engineers on RAG system design, prompt/evaluation methodology and production ML practices, and served as the primary technical point of contact with client stakeholders on solution strategy.

Built production LLM applications with embeddings, semantic search, prompt engineering and evaluation loops; improved retrieval/answer quality by 24% through chunking, reranking, prompt and evaluation optimization.

Developed predictive, classification, forecasting and anomaly-detection models with Python, PyTorch, TensorFlow and Scikit-learn; improved model performance by 13% through feature engineering and systematic tuning.

Implemented end-to-end ML/LLM pipelines with MLflow, Airflow, Docker and Kubernetes, reducing recurring deployment/training effort by 28% and improving reproducibility.

Built NLP/document-intelligence solutions for extraction, classification, entity recognition, summarization and conversational AI; reduced manual document handling by 33% in targeted workflows.

Automated cloud AI infrastructure with Terraform and CI/CD, reducing release cycle time by 26% and improving deployment consistency across AWS/Azure/GCP environments.

Partnered with engineering, product and business stakeholders across healthcare, finance and enterprise engagements to define success metrics, productionize solutions and translate technical results into business outcomes.

Michael Aaron Senior AI/ML Engineer

Machine Learning Engineer Sep 2021 - Jun 2023

Confirmed - AI/ML solutions spanning predictive analytics, customer intelligence, fraud detection and operational optimization

Developed predictive and fraud-detection models that improved detection precision by 16% while reducing false- positive review volume by 22% through feature engineering, threshold tuning and validation.

Built scalable preprocessing and feature-engineering pipelines with Python, SQL, Spark and cloud data platforms, cutting data-preparation time by 35% for recurring model workflows.

Developed transformer/BERT-based NLP models for text analytics and classification, improving F1 performance by 9% through fine-tuning, class balancing and evaluation-driven iteration.

Implemented computer-vision models with CNNs, OpenCV, TensorFlow and PyTorch for automated image analysis and classification use cases.

Built FastAPI ML services and deployed models with Docker, Kubernetes and CI/CD, reducing model release effort by 21% and enabling repeatable production deployments.

Worked with cross-functional teams to translate ambiguous business requirements into measurable ML objectives and production-ready solutions.

Data Scientist Jan 2020 - Aug 2021

Janus Robotics - AI/robotics startup focused on decision-support, public-health and infectious-disease applications

Built classification, regression and forecasting models supporting healthcare/public-health and enterprise analytics, improving predictive performance by 17% through feature engineering and model selection.

Performed EDA, statistical testing and feature analysis across structured and unstructured data to identify actionable business and operational signals.

Created NLP pipelines for document analysis, text processing and information extraction, reducing manual review effort by 27% in targeted workflows.

Developed Tableau, Power BI and SQL reporting products that reduced recurring reporting time by 45% and made model/operational insights accessible to non-technical stakeholders.

Collaborated with engineering teams to productionize analytical models, automate reporting and establish repeatable data workflows.

Junior Machine Learning Engineer Nov 2017 - Dec 2019 Neuromation - Enterprise AI transformation and MLOps platform/services

Supported ML model development and enterprise AI analytics across data preparation, experimentation, evaluation and deployment workflows.

Built reusable data-cleaning and preprocessing workflows that reduced repeated dataset preparation effort by 19%.

Performed feature engineering, model evaluation and statistical analysis to improve experiment quality and reproducibility.

Assisted with ML deployment and data-workflow maintenance in cloud and production-oriented environments. EDUCATION

Bachelor’s in Computer Science

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