Big Data Engineer

MLOPS (Machine Learning Operations): The MLOPS Engineer will design and build scalable machine learning infrastructure, ensuring smooth deployment, monitoring, and lifecycle management of ML models. • Responsibilities include automating workflows, enabling continuous integration/continuous deployment (CI/CD) pipelines. • Develop and Maintain ML Infrastructure: Build and maintain ML pipelines that support model training, testing, deployment, and monitoring. • Model Deployment: Implement efficient processes for deploying ML models in production environments, such as cloud platforms or on-premises infrastructure. • Set up CI/CD pipelines for continuous integration and delivery of ML models. Automation and Scaling: Automate model retraining, validation, and performance monitoring processes. • Collaboration with Data Scientists: Work closely with data scientists to streamline the model development lifecycle and ensure models can easily be transitioned to production. • Monitoring and Optimization: Monitor ML models in production for accuracy and performance and troubleshoot any deployment or scaling issues. • Infrastructure as Code (IaC): Develop infrastructure as code to manage cloud resources for ML workloads. • Versioning and Experimentation Tracking: Implement model versioning, experiment tracking, and reproducibility techniques. • Security and Compliance: Ensure models comply with organizational security standards and regulatory guidelines. • Containerization: Hands-on experience with Docker, Kubernetes, or other container orchestration systems. • CI/CD Tools: Knowledge of Jenkins, GitLab, CircleCI, or other CI/CD tools for automation. • Data Pipelines: Experience orchestration tools for managing data pipelines. • Version Control: Familiarity with Git for code versioning. • DevOps Experience: Basic understanding of DevOps tools and practices (e.g., Terraform) • 8+ years of experience on Python and AWS Services using ML Models Implementation to Production