Talex Maxim

Independent protocol development and AI security research involving anomaly detection, provenance graphs, and blockchain accountability.

AI engineering, AIOps, anomaly detection, and intelligent automation.

• Designed, implemented, and maintained scalable infrastructure using Infrastructure as Code (Terraform, Ansible), and container orchestration systems like Kubernetes (AKS, EKS, GKE).
• Developed and optimized continuous integration and delivery pipelines for seamless code deployments, reducing manual intervention and increasing efficiency across environments.
• Managed cloud infrastructures on AWS, Azure, and GCP, focusing on high availability, security, and cost-efficiency in production environments.
• ML workflows using platforms like SageMaker, Kubeflow, and MLflow. Automated model training, validation, and deployment processes, ensuring efficient scaling of machine learning models in production.
• Worked closely with data science and development teams to ensure smooth integration of ML models into production environments, ensuring performance, reliability, and scalability.
• Implemented monitoring solutions (Prometheus, Grafana, Datadog) for both infrastructure and ML models to ensure proactive issue detection and resolution, including anomaly detection for model behavior.
• Enforced security best practices across infrastructure, including the setup of automated vulnerability scans, access management, and data encryption.

• Built ML algorithm infrastructure to execute over big data.
• Optimized and parallelized ML algorithms to meet latency constraints on a parallel compute grid.
• Created integrated ML solutions for DevOps.

• Integrated ML algorithms into the decision-making process.
• Created ETLs with Apache Airflow and AWS Glue.
• Managed capacity planning, high availability engineering, and performance tuning.
• Created alerts, implemented monitoring tools, and improved visibility to prioritize and track operational metrics in team management.
• Developed and optimized build, release, and deployment toolchain.

• Updated and deployed machine learning models to production.
• Worked on data acquisition and developed solutions capable of handling large datasets.
• Developed data visualization tools for future exploration and model verification.

• Developed and maintained infrastructure for deploying ML models to production.
• Created tools for data visualization and model verification.
• Implemented data validation, security, access, and backup solutions.

• Designed and tested automated processes.
• Conducted integration and system tests using Java, Python, Ruby, Appium, and Selenium.