[Remote] Machine Learning Operations Engineer, AWS Stack

Note: The job is a remote job and is open to candidates in USA. Spark Tek Inc is seeking a Machine Learning Operations Engineer specializing in AWS Stack. The role involves deploying, monitoring, and supporting machine learning and computer vision solutions in production, collaborating with cross-functional teams to ensure reliable workflows and effective model management.

Responsibilities

• 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

Skills

• 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
• 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

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