Senior MLOps Engineer - CI/CD Pipeline

As a Senior MLOps Engineer with 8+ years of experience, you will be responsible for building and managing production-grade ML platforms and pipelines. Your expertise in AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows will be crucial for this role. Key Responsibilities: - Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation. - Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue). - Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda. - Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment. - Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus. - Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines. - Collaborate with data scientists to productionize notebooks, experiments, and model deployments. Qualifications Required: - 8+ years of experience in building and managing production-grade ML platforms and pipelines. - Strong expertise in AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. This job will require you to work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production. As a Senior MLOps Engineer with 8+ years of experience, you will be responsible for building and managing production-grade ML platforms and pipelines. Your expertise in AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows will be crucial for this role. Key Responsibilities: - Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation. - Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue). - Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda. - Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment. - Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus. - Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines. - Collaborate with data scientists to productionize notebooks, experiments, and model deployments. Qualifications Required: - 8+ years of experience in building and managing production-grade ML platforms and pipelines. - Strong expertise in AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. This job will require you to work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.