Machine Learning Engineer with AWS

Techneptune Consulting Inc

United States

3 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Amazon Web Services Amazon S3 Data Analysis Computer Vision Batch Processing Cloud Engineering Information Systems Continuous Integration Data Validation Information Engineering DevOps

+19 more

Monitoring of Systems Identity and Access Management Python (Programming Language) Machine Learning Operational Databases Release Management Cloud Services Workflow Management Systems AWS Cdk Delivery Pipeline State Machines Cloudformation Infrastructure Automation Frameworks Information Technology Machine Learning Operations Cloudwatch Terraform Software Version Control Docker

Requirements

Bachelor’s degree in computer science, engineering, data science, information systems, or a related technical field, or equivalent combination of education and relevant experience.
8+ years of experience in machine learning engineering, MLOps, cloud engineering, data engineering, DevOps, or production analytics support.
Practical experience working with AWS services used for machine learning or data workflows, such as Amazon S3, SageMaker, Lambda, Step Functions, CloudWatch, IAM, ECR, ECS, or related services.
Strong Python skills and comfort working with scripts, APIs, logs, configuration files, and version-controlled repositories.
Understanding of how machine learning models move from development into production, including model packaging, testing, deployment, monitoring, and support.
Experience supporting batch processing, inference pipelines, data validation, or production data workflows.
Familiarity with CI/CD concepts, source control, deployment coordination, and basic release management practices.
Ability to troubleshoot issues across data, code, cloud services, permissions, and operational workflows.
Ability to work across cross-functional teams and explain technical issues clearly to technical and business stakeholders.
Strong analytical, problem-solving, documentation, and communication skills., * Experience with computer vision, image-based analytics, inspection workflows, or large-scale image datasets.
Experience with Docker, container-based deployments, or model packaging for production use.
Exposure to infrastructure-as-code tools such as Terraform, CloudFormation, or AWS CDK.
Experience with model monitoring, data quality checks, operational dashboards, or alerting workflows.
Familiarity with ML lifecycle tools such as model registries, experiment tracking, or workflow orchestration.
Experience in utility, infrastructure, industrial inspection, or similar analytics environments using image-based data for decision-making is a strong advantage.