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Flex Consulting Group —Remote
- Email
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Data Science Consultant - AI Trainer
DataAnnotation —Albany, OR
- $50 - $100 an hour
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DataAnnotation —Bellevue, WA
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- Computer Science
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Data Science Consultant - AI Trainer
DataAnnotation —Canton, OH
- $50 - $100 an hour
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Data Science Consultant - AI Trainer
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Bright Vision Technologies —Remote
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13d
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Veeva Systems —Remote
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iCorps —Remote
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Technical Consultant
Cloven Media —Remote
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AWS ML/MLOps Consultant

Flex Consulting Group -
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Job Details

Full-time

Qualifications

Machine learning frameworks

Full Job Description

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We are looking for talented individuals who are passionate about making an impact in the world of consulting and technology. Explore our open positions below. Our clients needs in wide variety of industries. Please see open Job listing and select which is best fit for your skill sets. Apply for the Job and send your most updated resume and references.

AWS ML/MLOps Consultant

Location: Remote | Full-Time

We are looking for a AWS ML/MLOps Consultant. This engagement supports the development and maturation of a cloud-based MLOps capability designed to operationalize anomaly detection models for industrial equipment monitoring. The focus is on strengthening model performance, ensuring production reliability, and delivering scalable batch inference solutions within an AWS environment. MAIN RESPONSIBILITIES • Design and implement reusable feature engineering pipelines, including mechanisms to monitor data and model drift. • Improve the robustness, performance, and stability of existing anomaly detection models operating in production. • Partner with subject matter experts to validate model results and ensure alignment with real-world operational needs. • Establish tooling for experiment management, model versioning, and artifact storage to support repeatable ML workflows. • Build and deploy batch inference pipelines leveraging managed AWS services. • Connect model outputs to reporting, visualization, and alerting platforms for operational visibility. QUALIFICATIONS • Demonstrated experience delivering and supporting machine learning systems in production environments. • Experience designing and deploying batch-based inference workloads at scale. • Strong hands-on expertise with AWS services commonly used for MLOps, including SageMaker, ECS, and Step Functions. • Background in building end-to-end ML pipelines, from data ingestion through deployment and monitoring. • Working knowledge of gradient boosting frameworks such as LightGBM and XGBoost. • Prior exposure to industrial, energy, or asset performance monitoring use cases is a plus. Send resume to: [email protected]

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