Role overview
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.
What you'll work on
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.
What we're looking for
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