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
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