Principal AI/ML Engineer & MLOps Lead

Michael Trent

AI/ML Engineer Data Scientist Python Developer MLOps Practitioner

+1-720-***-**** *******.********@*****.*** https://www.linkedin.com/in/michael-trent/ Denver, CO SUMMARY

Principal AI/ML Engineer and Technical Lead with 12+ years of experience in enterprise AI, machine learning, and Generative AI across healthcare, energy, and regulatory domains. Delivered RAG-based LLM systems, agentic AI workflows, and production ML platforms that reduced manual review and research effort by 30–40% and improved decision accuracy by up to 35%. Architected scalable MLOps and cloud AI solutions across AWS, Azure, and GCP, improving deployment reliability by 40% and accelerating model release cycles by 30%.

EXPERIENCE

Principal AI/ML Engineer Team Lead

NewGen Strategies & Solutions Denver, CO

04/2023 – 10/2025

• Designed RAG-based regulatory chatbots using FAISS, GPT-4, and Gemini, reducing regulatory research and response time by 40%.

• At a healthcare knowledge automation project, I built LLM-driven clinical assistants using RAG pipelines, LangChain, vector databases, and Python FastAPI on Azure OpenAI, reducing manual clinical review effort by 35%.

• Developed deep learning models for aerial image classification and land-use detection using CNN architectures, improving classification accuracy by 30%.

• At an enterprise GenAI pilot, I developed agentic AI workflows using LangChain and AutoGen to orchestrate multimodal conversational agents, improving enterprise knowledge retrieval speed by 30%.

• At a clinical NLP modernization initiative, I implemented LLM prompt engineering and selective fine-tuning for summarization and entity extraction using AWS Bedrock, increasing downstream clinical data usability by 25%.

• Led the development of advanced load forecasting models using machine learning and time-series techniques, increasing forecast accuracy by 25%.

• At a regulated healthcare ML platform project, I built end-to-end MLOps pipelines using MLflow, Airflow, Docker, and Kubernetes, improving model deployment reliability by 40%.

• At a cloud AI migration project, I implemented CI/CD for ML and automated retraining workflows using Kubeflow and Helm on AWS and GCP Vertex AI, cutting model release cycles by 30%.

• Developed Python backend services and React.js frontends to connect AI/ML platforms with end users, improving model adoption and usability by 40%.

• At a client digital transformation engagement, I served as AI Architect, defining enterprise GenAI and MLOps reference architectures across AWS, Azure, and GCP, accelerating solution delivery timelines by 25%.

• Built cloud-based clustering pipelines for large-scale AMI datasets using Python and distributed ML frameworks, improving customer segmentation effectiveness by 28%.

• Designed and implemented CNN-based models for EV charging detection from AMI data, improving detection precision by 35%.

• At multiple cross-domain AI programs, I led architecture reviews, technical mentoring, and GenAI PoCs, guiding teams on secure, scalable AI design and improving overall platform adoption by 30%.

• Created interactive geospatial dashboards using React.js for the frontend and Node.js backend services, integrating data from multiple enterprise sources and reducing analyst reporting effort by 30%. Principal Machine Learning Engineer Data Scientist PA Consulting Denver, CO

01/2021 - 04/2023

• Designed and built AI/ML pipelines using TensorFlow and PyTorch to forecast wind and solar generation for renewable assets, improving prediction accuracy by 20% and reducing manual processing time.

• Developed ML models with Scikit-learn and XGBoost to analyze forward electricity price drivers, enhancing short-term price prediction accuracy by 15%.

• Created stochastic models using PyMC3 and NumPy to predict long-range electricity price volatility, improving risk assessment for utility trading strategies.

• Applied Markov-chain Monte Carlo and mean-reversion jump-diffusion models with PyMC3 to electricity price time series, reducing forecast error by 12%.

• Engineered ML models using PySpark and Scikit-learn to extract regional and LMP electricity price volatility drivers, enabling better operational planning.

• Built AI pipelines with TensorFlow and AWS Lambda/S3 to detect grid constraints and predict failure events, improving operational response times by 25%.

• Developed and updated proprietary utility-scale BESS models using Python and PyTorch, enhancing battery dispatch efficiency by 18%.

• Applied Bayesian models using PyMC3 to integrate imperfect foresight into BESS dispatch, improving storage optimization under uncertainty.

• Utilized Monte Carlo simulations and linear optimization in Python to build natural gas storage models, increasing storage utilization by 20%.

• Established hedge evaluation protocols using Python and NumPy to reduce financial risk exposure in energy trading by 10%.

• Pioneered stochastic price modeling procedures with PyMC3, improving reliability of long-term market price forecasts by 15%.

• Modernized utility-scale data pipelines using Snowflake, Python, and Airflow, improving data retrieval speed by 30% and supporting analytics at scale.

Machine Learning Engineer Principal Data Scientist LoneTree Energy & Associates Denver, CO

07/2013 - 01/2021

• Mined large client data sets using SQL and Excel to identify underpayment on oil and gas assets, recovering over $1M for clients.

• Developed data management solutions with Access and SQL databases to organize and streamline client data, improving reporting efficiency.

• Designed custom software to gather and consolidate data from disparate sources, reducing labor costs by

$150K/year.

• Engineered analytical software using Python and Excel to monitor market trends, well performance, and payment metrics, providing stakeholders with up-to-date insights.

• Established project management and analytical strategies, resulting in cost savings of $1.5M+ across multiple projects.

• Developed and maintained client relationships, contributing to business growth and retention across the portfolio.

• Managed the acquisition and integration of $500M+ in assets across four states, ensuring compliance and operational efficiency.

• Oversaw teams of 5–50 contractors on complex projects, coordinating tasks to meet deadlines and achieve project objectives.

Graduate Research Assistant

University of Colorado Boulder Boulder, CO

05/2011 - 08/2013

• Developed machine learning models to predict human movement in high-risk environments, improving experimental prediction accuracy by 25%.

• Built ML-based classifiers for fMRI data, reducing manual data labeling and analysis time by 30%.

• Designed and supported novel experimental protocols for human movement studies, enabling multiple peer-reviewed research studies.

• Created interactive virtual environments for controlled experiments, increasing participant engagement and data consistency by 20%.

• Developed custom data analysis software for large experimental datasets, cutting data processing time by 40%.

• Trained and supervised undergraduate research assistants, improving experiment throughput and data quality across multiple lab studies.

• Authored and co-authored peer-reviewed journal publications, contributing to the lab’s research output and external visibility.

EDUCATION

Ph.D. in Artificial Intelligence

University of Colorado Denver 2023 – 2026

Master’s Degree, Business Analytics

University of Colorado Denver 2016 – 2020

Master’s Degree, Computational Neuroscience

University of Colorado Boulder 2011 – 2013

Bachelor’s Degree, Mathematics

University of Colorado Boulder 2004 – 2008

Certification

Cleaning Data in Python

DataCamp Credential ID 52c555ecea9dced35fcc1238d79ba59744db2bba Data Types for Data Science in Python

DataCamp Credential ID 508a9157d105af955a45c92bca611545e9ced88e Supervised Learning with scikit-learn

DataCamp Credential ID 79899a3db04901f87c17a0217393516909b815aa SKILLS

Programming Languages: Python, TypeScript/JavaScript, SQL, Bash Machine Learning & Generative AI: TensorFlow, PyTorch, Scikit-learn, XGBoost, Transformers (Hugging Face), LLMs (GPT-4, Gemini, Claude), RAG Pipelines, Prompt Engineering, Fine-tuning, LangChain, LlamaIndex, AutoGen, Semantic Kernel, FAISS, Pinecone, Milvus, Weaviate, Bayesian Modeling (PyMC3), Time-Series Forecasting, Monte Carlo Simulation

MLOps & Model Serving: MLflow, Kubeflow, Airflow, CI/CD for ML, Model Monitoring & Drift Detection, Automated Retraining, Docker, Kubernetes, Helm, High-Performance Inference (vLLM, GPU Optimization) Full-Stack Development: FastAPI, Django, REST APIs, React.js, Authentication & Authorization, Observability & Logging, Backend Services for ML Platforms Cloud AI Platforms & DevOps: AWS (SageMaker, Bedrock, Lambda, S3, EKS), Azure (Azure ML, Azure OpenAI, Cognitive Services), GCP (Vertex AI, GKE), Terraform, GitHub Actions, Jenkins Data Engineering & Vector Systems: PySpark, Distributed ML Pipelines, Feature Stores, Lakehouse Architectures, Delta Lake, Iceberg, Snowflake, Data Quality & Lineage, Vector Databases, Semantic Search Architecture, Governance & Leadership: AI & GenAI Architecture, MLOps Platform Design, Microservices, Distributed Systems, Secure AI Design, Responsible AI, Privacy & Compliance in Regulated Environments, Technical Leadership, Mentoring, Client Advisory, Pre-Sales & PoCs Project & Delivery Practices: Agile/Scrum, Cross-Functional Collaboration, Architecture Reviews, Technical Documentation, Stakeholder Communication

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