Brittney Carr Email and Phone Number

• Engineered and deployed cloud-native infrastructure for small businesses, leveraging AWS and Azure to scale machine learning solutions.• Designed and implemented automated CI/CD pipelines for clients in tech, e-commerce, and healthcare, integrating Jenkins and Docker to accelerate ML model deployment and streamline releases.• Collaborated with clients to optimize cloud environments, achieving 99.9% uptime for critical machine learning applications and data pipelines.• Developed and maintained real-time monitoring systems using Grafana, reducing MTTR by 30% and ensuring the reliability of machine learning infrastructure.• Utilized configuration management tools such as Puppet and Chef to maintain consistent environments across development, testing, and production stages, ensuring robust ML model performance.• Implemented advanced Blue-Green and Canary deployment strategies, enabling zero-downtime rollouts and reducing risks during machine learning model updates.• Built a large-scale recommendation system for over 1,000 online learning platforms, utilizing Databricks (PySpark), MLFlow, and BigQuery to score billions of potential recommendations per run. Conducted A/B tests to improve recommendation accuracy and user engagement.• Developed a neural network-based ad recommendation engine, processing a dataset of 4M products and 18M customers, improving targeting accuracy and driving significant revenue increases.• Currently working on training and deploying end-to-end machine learning models to predict node failure repair recommendations, ensuring operational efficiency and system reliability.

• Provide end users tiered-level customer support by coordinating software, hardware, and networks; install, configure, troubleshoot, and provide maintenance and training.• Manage and secure commercial network Operating Systems (OS) within the functional areas of configuration, systems, and performance management• Identify vulnerabilities and perform corrective actions to ensure system confidentiality, integrity, and availability• Verify that appropriate security tests are conducted and documented, ensuring accreditation support documentation is developed and maintained; ensure that each information system meets security specifications to an acceptable level of risk.• Acquire, monitor, and maintain an organization’s COMSEC allowance; ensure proper storage and adequate physical security for all COMSEC material held within an account; operate and maintain the • • Management Client/Advanced Key Processor (MGC/AKP) to manage material, and support local key generation, encryption, decryption, and destruction.• Configure hardware and applications to interface with defended networks and Information Systems (IS) in support of Defensive Cyberspace Operations (DCO) on the Department of Defense Information Network (DODIN)• Utilize incident reporting procedures to investigate, analyze, and respond to disruptions within the pertinent domain to mitigate immediate and potential threats• Conduct threat and vulnerability assessments and determine deviations from acceptable configurations or policies. Assesses the level of risk and develops and/or recommends appropriate mitigation countermeasures in operational and non-operational situations.• Perform messaging, system monitoring, fault isolation, and circuit restoration of communications suites across the frequency spectrum• Current Secret clearance.

• Directed a critical migration from AKS to EKS, enhancing disaster recovery capabilities. Managed Terraform scripts for effective infrastructure transitions across Microsoft Azure and AWS. Automated CI/CD pipelines, monitoring systems, and deploying containerized solutions with Kubernetes and Docker, collaborating with development teams on enterprise cloud-native solutions.• Maintained, deployed, and managed applications with Kubernetes and Docker, focusing on container orchestration with Azure Kubernetes Service for optimal availability and scalability.• Partnered with application development teams throughout the software lifecycle, developing solutions for intricate challenges and fostering a team-centric environment. Integrated platform engineering and DevOps methodologies to refine development processes.• Guided the creation and implementation of enterprise-scale cloud-native solutions, blending platform engineering with software development. Emphasized resilient, scalable, and efficient cloud architecture to support various business applications.• Fully deployed ML Models in AWS using AWS Sagemaker and MLflow, overseeing the entire ML model lifecycle.• Managed custom ML models with Python in MLflow, incorporating MLflow Registry for model management and version control.• Developed an MLOps infrastructure on AWS, integrating MLFlow with services like EC2 and S3.• Implemented production workflows with Apache Spark, and embedded ML models into APIs using MLflow and FastAPI for scalable, high-performance solutions.• Employed MLflow and FastAPI for the creation of scalable, high-performance API interfaces• Leveraged libraries like Pandas for data manipulation, Scikit-learn for machine learning algorithms, and NumPy for numerical computations, to derive actionable insights and enhance model accuracy.

• Deliver top-tier front-line support to enterprise, business, and developer plan customers for container services, including EKS, ECS, Fargate, ECR, AppMesh, Batch, Prometheus (AMP), CloudMap, X-Ray, ParallelCluster, and Alexa, while adhering to case priorities and service level agreements (SLAs).• Act as a pivotal contributor in designing, developing, and supporting cloud-based mission-critical software applications, systems, and services for global companies by collaborating directly with engineering and technology teams.• Leverage cutting-edge technologies such as Amazon Web Services (AWS) and DevOps methodologies to promptly resolve software issues.• Serve as a trusted advisor on the complete suite of AWS products and the customer's architecture, providing assistance for strategy inquiries, project planning, implementation, and ongoing operations.• Assess how new AWS offerings align with customer architectures and proactively offer advice throughout all phases of the implementation lifecycle.• Proactively develop initiatives aimed at enhancing the company's software offerings and customer satisfaction by troubleshooting customer AWS cloud architectural challenges.• Implement AWS ML services (SageMaker, Rekognition, Comprehend) to build scalable ML models. • Assist in integrating these into AWS infrastructure for improved data analysis and decision-making.• Streamline ML workflows using AWS tools. Guide best practices in data preprocessing, model training, and deployment.• Collaborate in creating custom ML algorithms and models, using Python, TensorFlow, PyTorch within AWS.• Monitor ML model performance with AWS CloudWatch. Offer model tuning and retraining advice based on data and feedback.

• Work with system architects, network engineers, system engineers, software developers and database administrators to maintain/build servers and to perform troubleshooting, root cause analysis, problem analysis, and resolution of issues as they arise• Participate in all aspects of network and system operations daily including team meetings and troubleshooting calls• Keep leadership informed of status and always escalate potential areas of concern• Lead small workgroups and work assigned projects and daily assignments to successful completion• Coordination with business and technology teams on system/software requirements• Create/update system/software documentation• Research and make suggestions on the deployment of emerging technologies