Senior MLOps Engineer

*Position: Senior MLOps Engineer

Location: Reston, VA – 5 days onsite

Duration: Contract/Full Time**

• We are seeking an experienced and highly skilled AWS Full Stack ML Engineer to operationalize and optimize our large-scale financial modeling applications.
• This role requires a unique blend of expertise in machine learning, software engineering, and AWS cloud infrastructure, with a strong focus on implementing robust MLOps practices to ensure scalability, reliability, and cost-efficiency.
• The ideal candidate will bridge the gap between data science and production systems, transforming data science prototypes into secure, high-performance, and compliant solutions in a fast-paced financial environment.
• Implement MLOps and CI/CD: Design, build, and maintain end-to-end MLOps pipelines for the continuous integration, training, deployment, and monitoring of ML models on AWS.
• System Design and Integration: Reengineer large scale model development code (from data scientists) and model application code (from software engineers) and seamlessly integrate into unified, production-ready systems
• Automate Data Processing: Design and manage scalable and efficient ETL pipelines and data processing workflows for large-scale financial datasets, ensuring data quality and availability for model training and inference.
• Infrastructure Management: Utilize Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation to provision and manage secure, compliant, and reproducible ML infrastructure.
• Monitoring and Alerting: Implement robust monitoring, logging, and alerting frameworks (e.g., Amazon CloudWatch) to track model performance, data drift, and system health in production.
• Security and Compliance: Ensure all ML systems adhere to stringent financial industry regulations and security best practices (e.g., data encryption, IAM roles, VPC configurations).
• Optimize AWS Service Usage: Monitor and optimize AWS resource utilization to ensure cost-effectiveness, high availability, and performance for compute-intensive financial modeling applications.
• Collaboration: Work closely with cross-functional teams, including data scientists, data engineers, and software developers, to translate business requirements into technical solutions and champion MLOps best practices across the organization.