Entagile · 1 week ago
Full Stack AWS ML Engineer - Financial Modeling
United States
Full-time
Remote
Mid, Senior Level
4+ years expEntagile is seeking an experienced, highly skilled AWS Full-Stack ML Engineer to operationalize and optimize large-scale financial modeling applications. This role focuses on implementing robust MLOps practices and transforming data science prototypes into secure, high-performance solutions in a fast-paced financial environment.
Information Technology & Services
Hiring Manager
Victor Wang
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
Qualification
Required
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)
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
Preferred
Financial Domain Knowledge: Familiarity with the specific challenges and regulatory environment surrounding financial modeling and data is a strong plus
Certifications: AWS Certified Machine Learning - Specialty certification, AWS Certified Solutions Architect – Associate, or other relevant cloud certifications
Company
Entagile
Entagile LLC is a steadily growing financial and IT consulting firm that aims to become the best places - for top budding talents to work, grow and realize their career goals.
11-50 employees
H1B Sponsorship
Entagile has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (11)
2024 (7)
2023 (9)
2022 (16)
2021 (6)
Funding
Current Stage
Early StageCompany data provided by crunchbase