DevOps/MLOps Engineer, I specialize in integrating machine learning workflows with robust DevOps practices to streamline the development, deployment, and monitoring of AI/ML models. My role involves bridging the gap betw…
DevOps/MLOps Engineer, I specialize in integrating machine learning workflows with robust DevOps practices to streamline the development, deployment, and monitoring of AI/ML models. My role involves bridging the gap between data science and IT operations, ensuring seamless and efficient model deployment in production environments.
Led the development of an advanced continuous verification solution by implementing the CI/CD pipeline, observability, resilience testing, ML-driven QA with log analysis detection, and chaos engineering, achieving a 15% increase in reliability.
Delivery of machine learning models based on time series models and NLP models as a REST API.
Deploy the GCP function.
Create a WordPress plugin to retrieve data via the REST API from the Google Cloud function.
Automate the pipeline CI/CD via GitLab CI.
Automate all the processes via Terraform as IAC, which led to a 20% increase in reliability.
Develop anomaly detection models for test logs to reduce false positives using all-MiniLM-L11-v2 as embedding, autoencoders as unsupervised ML models, and apply a dynamic threshold to detect the anomalies that increase the reliability of the DevOps Pipeline by 15%.
Led Development of Advanced Continuous Verification Solution: Spearheaded the creation of a continuous verification system, enhancing model reliability by detecting and diagnosing issues in production. Delivered machine learning models as REST APIs, deployed robust GCP functions, and developed anomaly detection models to catch real-time irregularities.
Applied Modern MLOps Practices: Implemented modern MLOps principles, streamlining the CI/CD pipeline, automating model monitoring, and ensuring scalable, reproducible deployments in cloud environments.
Developed and implemented a continuous verification platform (VerifiQA), utilizing time series machine learning models like ARIMA and Prophet, which enhanced model performance and reliability.
Deploy end-to-end MLOps pipeline (CI/CD/CT) increased the efficiency of model deployment evaluation by 35%.
Developed and deployed an end-to-end MLOps pipeline enabling streamlined model development, testing, deployment, and monitoring, ensuring consistent and automated workflows across the entire ML lifecycle.
Built a continuous verification platform that automates data validation, model quality checks, and performance monitoring, improving model reliability and accuracy in production.
Led a project to develop, deploy, and maintain a robust time series forecasting model using a low-code machine learning library such as Prophet facebook.
Implemented modern MLOps practices, ensuring continuous integration, deployment, and monitoring, which enhanced overall project efficiency.
Led a comprehensive project to design, develop, deploy, and maintain a time series forecasting model focused on accurately predicting key business metrics. Responsibilities included defining project scope, establishing development and testing frameworks, and managing model iteration and evaluation.
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