[Remote] Machine Learning Engineer (AWS / MLOps / Data Engineering)

Note: The job is a remote job and is open to candidates in USA. Dice is seeking a Machine Learning Engineer to build, deploy, and scale production-grade machine learning solutions in a cloud-native AWS environment. The role focuses on designing ML architectures, developing end-to-end pipelines, creating inference workflows, operationalizing models, and improving machine learning platforms for scalability, reliability, performance, and cost optimization.

Responsibilities

• Design and implement scalable machine learning architectures on AWS
• Build and maintain end-to-end ML pipelines supporting model training, deployment, monitoring, and lifecycle management
• Develop real-time inference services and batch prediction workflows
• Create and enhance feature engineering pipelines and feature store ecosystems
• Collaborate with Data Science teams to operationalize machine learning models and maximize business impact
• Develop cloud-native solutions leveraging modern AWS services and Infrastructure as Code (IaC) practices
• Implement data processing frameworks for large-scale structured and unstructured datasets
• Improve existing machine learning platforms for scalability, reliability, performance, and cost optimization
• Partner with Data Engineering, Security, and Governance teams to ensure enterprise best practices
• Participate in Agile development processes, including sprint planning, stand-ups, code reviews, and continuous improvement initiatives

Skills

• Master's degree in Computer Science, Software Engineering, Data Science, or related technical field
• 5+ years of software engineering, machine learning engineering, or cloud engineering experience
• Strong experience building and deploying machine learning systems in production environments
• Advanced knowledge of AWS cloud services and cloud-native application design
• Python
• SQL
• PySpark
• Docker
• CI/CD Pipelines
• Infrastructure as Code (Terraform, CloudFormation, or similar)
• Experience working with both streaming and batch processing architectures
• Strong understanding of software engineering principles, system design, and distributed systems
• Experience within Agile software development environments
• Exceptional analytical, troubleshooting, and problem-solving skills
• Strong communication and collaboration abilities
• Possesses a solid software engineering foundation
• Takes a systematic and structured approach to problem-solving
• Learns new technologies quickly and adapts to evolving business needs
• Understands how to translate machine learning concepts into reliable production systems
• Enjoys collaborating across engineering, architecture, and data science teams
• Thrives in a fast-paced Agile environment
• Can clearly articulate technical decisions, architectural trade-offs, and implementation strategies
• Hands-on experience with AWS machine learning technologies such as SageMaker and related ML services
• Experience managing feature stores and feature engineering frameworks
• Background implementing MLOps practices and automated ML deployment pipelines
• Experience supporting large-scale recommendation, prediction, optimization, or AI-driven platforms
• Familiarity with model monitoring, observability, and ML governance
• Exposure to enterprise-scale data platforms and analytics ecosystems

Company Overview

Company H1B Sponsorship