MLOps Engineer with 2 years of hands-on experience building, deploying, and monitoring machine learning solutions. Experienced in automating ML pipelines, containerization, CI/CD, model versioning, and cloud-based deployments using Python, MLflow, Docker, Kubernetes, Azure Machine Learning, GitHub Actions, and FastAPI.
Expertise & skills
Selected projects
ML Model Deployment Platform
Developed a centralized platform for deploying and managing machine learning models.
- Containerized ML models using Docker.
- Created FastAPI inference services.
- Integrated MLflow Model Registry.
- Automated deployment using GitHub Actions.
- Monitored model performance after deployment.
Automated Training Pipeline
Built an end-to-end automated ML training pipeline for structured datasets.
- Developed data preprocessing workflows.
- Tracked experiments using MLflow.
- Automated model retraining.
- Stored artifacts in Azure Storage.
- Validated model performance before deployment.
Fraud Detection Monitoring Dashboard
Created monitoring dashboards to track fraud detection model performance.
- Monitored prediction drift.
- Generated performance reports.
- Configured alert mechanisms.
- Tracked inference latency.
- Documented monitoring procedures.
Customer Segmentation Pipeline
Built an automated segmentation workflow for marketing analytics.
- Prepared customer datasets.
- Scheduled model execution.
- Published prediction APIs.
- Managed model versioning.
- Optimized deployment workflows.
Ways of working
Certifications
- Microsoft Certified: Azure AI Fundamentals (AI-900)
- Microsoft Certified: Azure Fundamentals (AZ-900)
- Docker Essentials
- Machine Learning Operations Fundamentals
Education
- 2024
Master of Technology (M.Tech.) – Artificial Intelligence
Gujarat Technological University (GTU)
- 2022
Bachelor of Engineering (B.E.) – Computer Engineering
Gujarat Technological University (GTU)
Achievements
- Automated ML deployment pipelines using CI/CD.
- Improved deployment consistency through containerization.
- Implemented model versioning and experiment tracking.
- Built scalable cloud-based ML deployment solutions.
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