Description

• Support the deployment and day-to-day operation of machine learning and computer vision models used for inspection and asset intelligence use cases, including overhead equipment inspection and unauthorized attachment detection.
• Partner with data scientists and machine learning engineers to package approved models for production use and make sure model handoffs are clear, tested, and documented.
• Build and maintain practical AWS-based workflows for data movement, model execution, batch inference, and output delivery using services such as Amazon S3, SageMaker, Lambda, Step Functions, CloudWatch, and related AWS tools.
• Help create repeatable deployment processes so models can move from development to testing to production in a controlled and consistent way.
• Support CI/CD practices for machine learning workflows, including code versioning, automated checks, deployment readiness steps, and release coordination.
• Monitor production model runs for job completion, data issues, system errors, performance changes, and operational readiness.
• Assist with troubleshooting production inference issues by reviewing logs, validating inputs and outputs, coordinating fixes, and communicating status to stakeholders.
• Maintain clear runbooks, deployment notes, monitoring summaries, and support documentation so production workflows can be operated consistently by the broader team.
• Work with product managers, SMEs, data teams, cloud platform teams, and business stakeholders to align on production requirements, release timing, support needs, and success measures.
• Help improve reliability, scalability, security, and cost awareness for machine learning workloads without over-engineering the solution.

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.
• 3+ 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

Key Skills

Education

Bachelor's degree