RX2 Solutions
Employer Industry: Cloud Computing and Machine Learning
Why consider this job opportunity:
100% remote work opportunity, providing flexibility in your work environment
Engaging in cutting-edge technologies within the cloud-based MLOps domain
Opportunity to work on innovative projects related to industrial equipment monitoring
Collaborate with subject matter experts and contribute to real-world operational needs
Chance to enhance machine learning systems and improve model performance
What to Expect (Job Responsibilities):
Design and implement reusable feature engineering pipelines to monitor data and model drift
Improve the robustness, performance, and stability of existing anomaly detection models in production
Partner with subject matter experts to validate model results and ensure operational alignment
Establish tooling for experiment management, model versioning, and artifact storage to support ML workflows
Build and deploy batch inference pipelines utilizing managed AWS services
What is Required (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
How to Stand Out (Preferred Qualifications):
Prior exposure to industrial, energy, or asset performance monitoring use cases
#CloudComputing #MachineLearning #RemoteWork #AWS #MLOps
We prioritize candidate privacy and champion equal‑opportunity employment. Central to our mission is our partnership with companies that share this commitment. We aim to foster a fair, transparent, and secure hiring environment for all. If you encounter any employer not adhering to these principles, please bring it to our attention immediately. We are not the EOR (Employer of Record) for this position. Our role in this specific opportunity is to connect outstanding candidates with a top‑tier employer.
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Why consider this job opportunity:
100% remote work opportunity, providing flexibility in your work environment
Engaging in cutting-edge technologies within the cloud-based MLOps domain
Opportunity to work on innovative projects related to industrial equipment monitoring
Collaborate with subject matter experts and contribute to real-world operational needs
Chance to enhance machine learning systems and improve model performance
What to Expect (Job Responsibilities):
Design and implement reusable feature engineering pipelines to monitor data and model drift
Improve the robustness, performance, and stability of existing anomaly detection models in production
Partner with subject matter experts to validate model results and ensure operational alignment
Establish tooling for experiment management, model versioning, and artifact storage to support ML workflows
Build and deploy batch inference pipelines utilizing managed AWS services
What is Required (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
How to Stand Out (Preferred Qualifications):
Prior exposure to industrial, energy, or asset performance monitoring use cases
#CloudComputing #MachineLearning #RemoteWork #AWS #MLOps
We prioritize candidate privacy and champion equal‑opportunity employment. Central to our mission is our partnership with companies that share this commitment. We aim to foster a fair, transparent, and secure hiring environment for all. If you encounter any employer not adhering to these principles, please bring it to our attention immediately. We are not the EOR (Employer of Record) for this position. Our role in this specific opportunity is to connect outstanding candidates with a top‑tier employer.
#J-18808-Ljbffr