AWS Full Stack ML Engineer (Financial Modeling)

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. Key Responsibilities 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. Code Integration: Seamlessly integrate model development code (from data scientists) and model application code (from software engineers) 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. Optimize AWS Service Usage: Monitor and optimize AWS resource utilization to ensure cost-effectiveness, high availability, and performance for compute-intensive financial modeling applications. 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). 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. Required Skills and Qualifications Experience: Proven experience (4+ years preferred) in MLOps, DevOps, or a related role, with hands-on experience deploying ML applications at scale. Programming Proficiency: Strong proficiency in Python and relevant ML libraries/frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). AWS Expertise: In-depth experience with key AWS services for ML and data, including Amazon SageMaker, S3, EC2, EKS/Fargate, Lambda, AWS Glue, and IAM. MLOps Tools: Experience with containerization (Docker), orchestration (ECS/Kubernetes/EKS), CI/CD tools (GitLab, AWS CodePipeline, Jenkins), and workflow orchestrators (Apache Airflow or AWS Step Functions). Financial Domain Knowledge (Preferred): Familiarity with the specific challenges and regulatory environment surrounding financial modeling and data is a strong plus. Software Engineering Best Practices: Solid understanding of software development lifecycle, including testing, debugging, version control (Git), and code quality standards. Problem-Solving: Excellent analytical and problem-solving skills, with the ability to troubleshoot complex, interconnected systems. Education: A Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field Certifications (Preferred): AWS Certified Machine Learning - Specialty certification, AWS Certified Solutions Architect - Associate, or other relevant cloud certifications.

Created: 2026-03-04