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:

Bachelor s or Master s degree in Computer Science, Engineering, or a related field.
Experience: 5+ years of experience as an MLOps Engineer, DevOps Engineer, or similar role focused on machine learning deployment and operations.
Strong expertise in AWS services, particularly SageMaker, EC2, S3, Lambda, and ECR/ECS/EKS.
Proficiency in Python, including ML-focused libraries like scikit-learn and data manipulation libraries like pandas.
Hands-on experience with containerization tools such as Docker and Kubernetes.
Familiarity with infrastructure as code (IaC) tools such as Terraform or AWS CloudFormation.
Experience with CI/CD pipelines, Git, and version control for ML model deployment.
MLOps & Model Management: Proven experience in managing large ML projects, including model deployment, monitoring, and maintenance.
AWS Connect & Integration: Understanding of AWS Connect for customer interactions and integration with ML models.
Soft Skills: Strong communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders.
Experience with data streaming and message queues (e.g., Kafka, AWS Kinesis).
Familiarity with monitoring tools like Prometheus, Grafana, or CloudWatch for tracking model performance.
Knowledge of data governance, security, and compliance requirements related to ML data handling.
Certification in AWS or relevant cloud platforms.
Work Schedule: This role requires significant overlap with CST time zone to ensure real-time collaboration with the team and stakeholders based in the U.S. Flexibility is key, and applicants should be available for meetings and work during U.S. business hours.

Senior Engineer MLops