Senior Machine Learning Engineer

• Championed AI-driven virtual care innovations, harnessing deep learning frameworks (TensorFlow, PyTorch, Hugging Face Transformers) to fortify patient monitoring capabilities by 30% while adhering to 100% HIPAA regulations. • Orchestrated machine learning pipelines with Python, GCP Vertex AI and AWS SageMaker/Bedrock to process real-time virtual care data, boosting high-risk event detection accuracy by 15% and efficiency by 25%. • Pioneered computer vision algorithms within React web applications, employing Redux for state management and TypeScript for SPAs, slashing patient monitoring response time by 20%. • Designed LLM evaluation frameworks to measure model performance across automated + human-in-the-loop setups, accelerating experimentation by 35%. • Built feedback tools for structured/unstructured data integrated into clinical workflows, boosting virtual nursing accuracy by 20%. • Developed ML pipelines in Databricks with PySpark, enabling large-scale preprocessing and real-time streaming of patient data, reducing latency by 30%. • Enhanced observability with Prometheus, ELK Stack, and Grafana dashboards for patient-monitoring AI systems, improving issue resolution time by 40%. • Partnered with product owners and data scientists to deliver LLM-powered RAG workflows and Agentic AI in LangChain, achieving a 40% uplift in AI-driven triage and autonomous task handling. • Collaborated with cross-functional teams to deploy scalable AI models on Kubernetes using Helm charts, integrating NLP and GenAI through LangChain to analyze patient interactions, and achieved a 40% improvement in virtual nursing applications with RAG workflows. • Optimized AI models for bedside virtual care assistants, focusing on triage of patient requests and enhancing overall quality of care by 35% through data-driven insights with Scikit-learn for statistical modeling and leveraging Snowflake as a data warehouse. • Designed and deployed multi-agent systems for model observability and context reasoning, reducing false alerts in patient monitoring by 32%. • Mentored 10 junior team members on best practices in MLOps, ensuring seamless deployment and maintenance of machine learning systems in high-pressure environments using Docker for containerization and GitHub Actions for CI/CD pipelines, reducing deployment errors by 90%. • Integrated backend services using Go (Golang) and Kafka, designing event schemas, implementing producers/consumers, and improving throughput by 50% for real-time event-driven architecture with PostgreSQL schema management. • Integrated autonomous data orchestration agents for triage and workflow coordination, increasing AI-driven task execution efficiency by 35% and reducing manual interventions by 45%. • Enhanced AI-driven patient monitoring by integrating GCP Vertex AI and Azure Cognitive Services for advanced computer vision and NLP, using Python with TensorFlow and Hugging Face for model development, achieving 20% higher model accuracy. • Created React-based dashboards with Material UI and Tailwind CSS, paired with Node.js/Express backend services and FastAPI for high-performance APIs, enabling real-time visualization of patient data processed through GCP BigQuery and utilizing Weaviate’s vector DB, improving data retrieval by 30%. • Activated serverless ML inference endpoints using GCP Cloud Run, Vertex AI, Azure Functions, and MLflow for model tracking, ensuring scalable and cost-efficient AI operations with Redis for caching, reducing latency by 15%.

Full-Stack Machine Learning Engineer

• Developed algorithms using machine learning and statistical modeling techniques with PyTorch, Scikit-learn, and Hugging Face to enhance system performance by 20% and data accuracy by 15% in pharmaceutical return processes, integrating GenAI for predictive analytics. • Evaluated and refined predictive models to optimize quality control and data management within the supply chain operations, deploying them via AWS machine learning pipelines including Bedrock and GCP Vertex AI for LLM fine-tuning, reducing errors by 10%. • Implemented systematic analysis tooling for logged predictions, uncovering error patterns that reduced false positives in pharma returns by 15%. • Deployed predictive models with AWS Bedrock + Databricks pipelines, cutting operational inefficiencies by 20%. • Developed feedback-driven Agentic AI pipelines using LangChain and Vertex AI to refine predictions and trigger automated data actions, improving operational intelligence and reducing manual review by 30%. • Designed self-running AI software that automated predictive models, reducing manual intervention by 70% and improving operational efficiency by 25% through deep learning integrations with Node.js/Express backend and RESTful/GraphQL APIs. • Leveraged NoSQL (MongoDB) and BigQuery for model logging, enabling faster audit trails and increasing compliance confidence by 30%. • Incorporated deep learning components into full-stack applications using Angular for frontend dashboards with Context API, handling complex data processing tasks related to inventory and returns, improving user satisfaction by 30% with Pinecone for RAG-based systems. • Conceptualized AWS for deploying scalable machine learning pipelines, ensuring robust and secure handling of sensitive pharmaceutical data with Python and Go backend scripts, achieving 95% uptime with Kafka topics and partitions for event streaming. • Pioneered the integration of NLP for return documentation analysis and MongoDB for NoSQL storage, facilitating a 20% faster processing time and identifying 3 key bottlenecks. • Constructed RESTful APIs in Go with performance tuning and concurrency patterns, facilitating seamless data flow and real-time predictions with 99% reliability, while managing database migrations and integrity in PostgreSQL with complex SQL queries. • Streamlined predictive analytics with Azure Machine Learning for model training and GCP BigQuery for data processing, using Python with Scikit-learn and Hugging Face Transformers for NLP tasks, improving forecast accuracy by 12%. • Spearheaded the integration of React UIs with TypeScript and Redux, linking Node.js and FastAPI to Milvus for vector embeddings, sharpening inventory tracking by 35%. • Implemented serverless architectures with Azure Functions and GCP Cloud Functions, using Kubeflow for ML pipeline orchestration and Redis cache optimization, cutting operational costs by 20%.

Senior Software Engineer

• Developed automated testing frameworks in Python and Node.js that accelerated the QA process by 10% and increased productivity by 10% from card to demo stages, incorporating CI/CD with GitHub Actions. • Realized an access database system with user-friendly forms built in React using TypeScript and Next.js, enabling efficient tracking of aircraft parts inventory, reducing manual effort by 40% through interactive frontend components with Redux. • Assembled full-stack web applications to analyze and process client data, incorporating secure interactions with a suite of Go-based services interacting with APIs and databases for backend logic and GraphQL for efficient querying, improving data processing speed by 15%. • Spearheaded the migration of legacy systems to AWS, fortifying cloud security with Docker and Kubernetes, and achieved a 99.9% system uptime while diminishing infrastructure costs by 20%. • Guided the integration of TensorFlow and LangChain to apply deep learning models for data analysis and GenAI features, enhancing the user experience by 20% according to user feedback. • Coordinated with teams to integrate machine learning elements where applicable, reflecting expertise in deep learning for data processing with prompt engineering and bias detection in LLM workflows, reducing bias by 25%. • Shaped Angular-based user interfaces for client-facing tools, enhancing data visualization and interaction with backend services using FastAPI and Snowflake for data pipelines, improving user engagement by 20%. • Merged GCP Cloud Build and Azure DevOps for CI/CD pipelines, using Python + TensorFlow powering AI-driven data analysis and Node.js with Express.js for scalable APIs, improving deployment speed by 15%. • Delivered React-based dashboards with Material UI, paired with GCP Dataflow for ETL processes and Weaviate for vector search, enhancing data visualization for aircraft inventory tracking by 18%. • Championed Kubeflow pipeline orchestration while using AML to deliver ML models, employing Redis for caching, which bolstered AI model updates by 15% in evolving environments

Machine Learning Engineer | Founder

• Designed and implemented an in-house algorithm for inventory tracking using advanced computer vision with OpenCV, PyTorch, and Hugging Face, deployed on GCP AI Platform and Vertex AI, pioneering innovations in intelligent inventory management via API integrations and RAG, reducing errors by 50%. • Led the patent process for the inventory system with the USPTO, demonstrating leadership in developing cutting-edge AI solutions that incorporated deep learning frameworks and GenAI models like those from OpenAI, achieving patent approval in 12 months. • Built a recommendation engine that analyzed user preferences and behaviors using NLP techniques in Python with LangChain, achieving a 40% reduction in food wastage through personalized recommendations and vector embeddings in FAISS. • Developed autonomous data orchestration pipelines using Agentic AI frameworks (LangChain + OpenAI APIs), enhancing workflow automation and decision accuracy by 30%. • Produced a software tool to track preferred food prices in nearby stores, helping users save an average of 21% annually on grocery shopping, with backend processing in Go for efficiency and Kafka for event-driven updates, serving 500+ users. • Managed full lifecycle of 4 AI projects, from conception to deployment, utilizing machine learning pipelines and deep learning techniques on AWS infrastructure with Kubernetes orchestration and Terraform for IaC, completing projects 95% on schedule. • Piloted the development of user dashboards incorporating React frontend with Material UI, integrating GraphQL APIs, fostering alignment among 10 stakeholders and resolving challenges within a startup context. • Unified Angular components for strengthened user experience in recommendation apps, ensuring seamless interaction with ML backend services using Node.js, Express, and PostgreSQL for schema design and performance optimization, boosting adoption by 35%. • Upgraded inventory tracking with GCP AI Platform and Azure Blob Storage for scalable data handling, using Python with OpenCV and Hugging Face Transformers for computer vision, improving processing speed by 22%. • Engineered React UIs with TypeScript + Redux, enabling Weaviate-powered RAG workflows via Node.js/Express, boosting inventory update efficiency by 20%. • Launched serverless ML models with GCP Cloud Functions, Vertex AI, Azure Functions coupled with MLflow for lifecycle management, using Redis for caching and Kubeflow for pipeline management, reducing latency by 18%.

Capitol Technology University

Maryville University of Saint Louis

University of Miami