MLOPS Engineer with AWS Stack

Role: Machine Learning Operations Engineer, AWS Stack Location :CA Bay Area Locals (Remote) Duration: 12 Months Department Overview What You'll Do • 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. • 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.