L1 Support (Ray)

Austin - USA

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Role Overview:
We are seeking an MLOps Engineer with strong expertise in distributed systems ML model operations and performance optimization. The role involves working with frameworks like Ray and Flink automating ML pipelines and ensuring reliable deployment and scaling of machine learning workloads. A solid background in scripting troubleshooting and performance tuning is essential.

Key Responsibilities:

Design implement and maintain MLOps pipelines for training deployment and monitoring of ML models.
Work with distributed computing frameworks like Ray and Flink to scale machine learning workloads.
Troubleshoot infrastructure and model deployment issues ensuring reliability and efficiency.
Perform performance tuning for both ML pipelines and distributed compute clusters.
Automate workflows and monitoring using Python and Shell scripting.
Collaborate with data scientists and ML engineers to optimize ML model tuning and hyperparameter search.
Implement best practices for CI/CD in ML model versioning and reproducibility.
Monitor model performance in production and drive continuous improvements.

Qualifications & Skills:

Bachelors or Masters degree in Computer Science Data Engineering or related field.
3 years of experience in MLOps DevOps or Data Platform Engineering roles.
Hands-on experience with Ray (for distributed ML training) and Flink (for streaming data processing).
Strong troubleshooting and performance tuning skills in distributed systems.
Proficiency in Python and Shell scripting for automation and integration.
Solid understanding of ML lifecycle including training tuning deployment and monitoring.
Familiarity with containerization (Docker Kubernetes) and cloud environments (AWS Azure or GCP).

Nice-to-Have:

Experience with MLflow Kubeflow or other MLOps platforms.
Exposure to hyperparameter tuning frameworks (Optuna Ray Tune).
Knowledge of logging/monitoring stacks (Prometheus Grafana ELK or similar).
Background in distributed data systems (Spark Kafka).

Pi-square technologies is a Michigan (USA) Headquartered Automotive Embedded Engineering Services company Synergy Partner for major OEMs and Tier 1s and their implementation partners in Automotive Embedded Product Development Projects Requirements Analysis Software Design Software Implementation Efficient Build Release Process and turnkey software V & V Services. We have more than 20 years of industry expertise with specialization in the latest cutting-edge automotive technologies such as Infotainment connected vehicles Cyber security OTA and Advanced Safety/ Body electronics.

Role Overview: We are seeking an MLOps Engineer with strong expertise in distributed systems ML model operations and performance optimization. The role involves working with frameworks like Ray and Flink automating ML pipelines and ensuring reliable deployment and scaling of machine learning workloa...

Key Responsibilities:

Design implement and maintain MLOps pipelines for training deployment and monitoring of ML models.
Work with distributed computing frameworks like Ray and Flink to scale machine learning workloads.
Troubleshoot infrastructure and model deployment issues ensuring reliability and efficiency.
Perform performance tuning for both ML pipelines and distributed compute clusters.
Automate workflows and monitoring using Python and Shell scripting.
Collaborate with data scientists and ML engineers to optimize ML model tuning and hyperparameter search.
Implement best practices for CI/CD in ML model versioning and reproducibility.
Monitor model performance in production and drive continuous improvements.

Qualifications & Skills:

Bachelors or Masters degree in Computer Science Data Engineering or related field.
3 years of experience in MLOps DevOps or Data Platform Engineering roles.
Hands-on experience with Ray (for distributed ML training) and Flink (for streaming data processing).
Strong troubleshooting and performance tuning skills in distributed systems.
Proficiency in Python and Shell scripting for automation and integration.
Solid understanding of ML lifecycle including training tuning deployment and monitoring.
Familiarity with containerization (Docker Kubernetes) and cloud environments (AWS Azure or GCP).