Senior Machine Learning DevOps Engineer (MLOps-Hiring for client- Min Exp Req is 8 years))

Role & Responsibilities We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production. Key Responsibilities: Minimum 8 years of experience MLOps role is required. Please

dont apply if you have less exp. 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. Ideal Candidate 8+ years in MLOps/DevOps with strong ML pipeline experience. Strong hands-on experience with AWS: Compute/Orchestration: EKS, ECS, EC2, Lambda Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis Workflow: MWAA/Airflow, Step Functions Monitoring: CloudWatch, OpenSearch, Grafana Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn). Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins). Strong Linux, scripting, and troubleshooting skills. Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.