I’m a professional MLOps Engineer with a strong background in deploying, automating, and scaling machine learning models in production environments. I specialize in building robust ML systems that move models from development to deployment reliably, efficiently, and at scale.
Whether you're a startup trying to get your first model into production, or an enterprise looking to optimize existing workflows, I help streamline the full machine learning lifecycle—from data ingestion to monitoring deployed models.
My key offerings include:
🔧 End-to-End MLOps Pipelines (CI/CD for ML)
🚀 Model Deployment (REST APIs, Batch Jobs, Real-Time Inference)
☁️ Cloud-Native ML Solutions (AWS SageMaker, GCP Vertex AI, Azure ML)
📦 Containerization & Orchestration (Docker, Kubernetes, Helm)
🔄 Experiment Tracking & Versioning (MLflow, DVC, Weights & Biases)
🧠 Data & Feature Engineering Pipelines (Airflow, Spark, dbt)
📊 Monitoring & Logging (Prometheus, Grafana, ELK Stack)
🧪 Testing & Automation (Unit Testing, Integration Testing for ML)
I work closely with teams to automate training, testing, and deployment, ensuring ML systems are repeatable, traceable, and production-ready. With experience across industries and platforms, I provide tailored MLOps solutions that align with your business goals.
Let’s build scalable, efficient, and reliable ML infrastructure together.