Machine Learning Engineer with AWS

What You Bring

• 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.

Desired Qualifications

• 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.