Hire Akash D. - MLOps Engineer

* Built and maintained modular ML pipelines orchestrated via Apache Airflow , enabling automated data processing, model training, and deployment workflows. * Utilized MLflow to track experiments, manage model metadata, and streamline model versioning and deployment lifecycle. * Designed scalable solutions for batch and real-time inference using containerized ML models hosted on AWS EC2 and Kubernetes . * Developed custom DAGs in Airflow to trigger ML jobs based on upstream data availability, model performance thresholds, or user inputs. * Automated model retraining and deployment based on performance decay using a combination of Airflow sensors and MLflow metrics . * Led initiatives to implement model governance, auditability, and reproducibility * aligning with industry MLOps standards.

* Designed and deployed end-to-end machine learning pipelines using Amazon SageMaker for model training, deployment, and monitoring in production environments. * Automated data extraction and transformation using AWS Glue , streamlining ingestion from diverse sources. * Orchestrated ML workflows with AWS Step Functions and automated tasks using AWS Lambda for scalable and maintainable solutions. * Implemented model monitoring and drift detection to ensure ongoing model reliability and performance in real-time applications. * Built CI/CD pipelines for ML using Git and AWS CodePipeline , supporting continuous training and deployment. * Optimized pipelines for efficiency, fault-tolerance, and low-latency , enabling real-time predictions for high-impact business use cases. * Collaborated cross-functionally with data scientists and Data engineering teams to drive ML adoption at scale.