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.
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.
Required Skills and Qualifications
Experience:Proven experience (6+ years preferred) in MLOps, DevOps, or a related role, with hands-on experience in developing and 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//EKS), CI/CD tools (GitLab, AWS CodePipeline, Jenkins), and workflow orchestrators (AWS Step Functions, Apache Airflow ).
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 system design, microservice implementation, 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.
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