Description

You will operationalize and optimize large-scale financial modeling applications by bridging data science and production systems.

Responsibilities

• Design, build, and maintain end-to-end MLOps pipelines for model training, deployment, and monitoring on AWS.
• Integrate model development code and application code into unified, production-ready systems.
• Design and manage scalable ETL pipelines for large-scale financial datasets, ensuring data quality.
• Utilize IaC tools like Terraform or AWS CloudFormation to provision secure ML infrastructure.
• Implement monitoring, logging, and alerting frameworks to track model performance and system health.

Required Skills

• 5+ years of experience in MLOps, DevOps, or a related role, deploying ML applications at scale.
• Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, SciKit-learn).
• In-depth experience with AWS services, including SageMaker, S3, EC2, Lambda, and IAM.
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Familiarity with CI/CD tools (GitLab, AWS CodePipeline, Jenkins).
• Solid understanding of software development lifecycle, version control (Git), and testing.
• Excellent analytical and problem-solving skills for complex system troubleshooting.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.

Key Skills

Education

Any Graduate