Senior Engineer MLops

As an MLOps Engineer, you will be responsible for building and optimizing our machine learning infrastructure. You will leverage AWS services, containerization, and automation to streamline the deployment and monitoring of ML models. Your expertise in MLOps best practices, combined with your experience in managing large ML operations, will ensure our models are effectively deployed, managed, and maintained in production environments.

Responsibilities :

• Machine Learning Operations (MLOps) & Deployment :
• Build, deploy, and manage ML models in production using AWS SageMaker, AWS Lambda, and other relevant AWS services.
• Develop automated pipelines for model training, validation, deployment, and monitoring to ensure high availability and low latency.
• Implement best practices for CI / CD in ML model deployment and manage versioning for seamless updates.
• Infrastructure Development & Optimization :
• Design and maintain scalable, efficient, and secure infrastructure for machine learning operations using AWS services (e.g., EC2, S3, SageMaker, ECR, ECS / EKS).
• Leverage containerization (Docker, Kubernetes) to deploy models as microservices, optimizing for scalability and resilience.
• Manage infrastructure as code (IaC) using tools like Terraform, AWS CloudFormation, or similar, ensuring reliable and reproducible environments.
• Model Monitoring & Maintenance :
• Set up monitoring, logging, and alerting for deployed models to track model performance, detect anomalies, and ensure uptime.
• Implement feedback loops to enable automated model retraining based on new data, ensuring models remain accurate and relevant over time.
• Troubleshoot and resolve issues in the ML pipeline and infrastructure to maintain seamless operations.
• AWS Connect & Integration :
• Integrate machine learning models with AWS Connect or similar services for customer interaction workflows, providing real-time insights and automation.
• Work closely with cross-functional teams to ensure models can be easily accessed and utilized by various applications and stakeholders.
• Collaboration & Stakeholder Engagement :
• Collaborate with data scientists, engineers, and DevOps teams to ensure alignment on project goals, data requirements, and model deployment standards.
• Provide technical guidance on MLOps best practices and educate team members on efficient ML deployment and monitoring processes.
• Actively participate in project planning, architecture decisions, and road mapping sessions to improve our ML infrastructure.
• Security & Compliance :
• Implement data security and compliance measures, ensuring all deployed models meet organizational and regulatory standards.
• Apply appropriate data encryption and manage access controls to safeguard sensitive information used in ML models.

Requirements :

Skills Required

Docker, Terraform, Kubernetes, Python