Senior DevOps Engineer

 

 

Overview and Responsibilities:

 

We are looking for a Senior DevOps Engineer - Knowledge Graph & Asset Embedding Systems to join our Personalization Team. This role will focus on core backend infrastructure for personalization of feature engineering and search, ensuring high-performance computing for knowledge graph-based recommendations and asset embedding systems. The ideal candidate will have experience working with Kubernetes, Ray Clusters, TensorFlow (TF), Prometheus, and high-performance parallel computing to support large-scale ML workloads.

 

Responsibilities Include:

· Design, implement, and manage scalable infrastructure for knowledge graph and asset embedding pipelines.

· Optimize Kubernetes-based deployments for ML feature engineering and real-time inference.

· Develop and maintain Ray Clusters to support distributed ML workloads for embeddings and graph processing.

· Automate CI/CD pipelines to streamline the deployment of ML models and feature engineering services.

· Implement observability and monitoring solutions using tools like Prometheus, Datadog, and OpenTelemetry

· Ensure high availability, security, and performance of ML feature pipelines.

· Work closely with ML engineers to deploy and scale knowledge graph-based personalization services.

· Optimize ML infrastructure for TensorFlow-based model training and serving.

· Implement autoscaling strategies for high-performance ML feature computation.

· Debug and resolve production issues related to latency, scaling, and reliability.

Key Projects:

· Build and optimize scalable feature engineering pipelines for personalization and search.

· Develop high-performance knowledge graph processing infrastructure.

· Implement real-time asset embedding systems for recommendation models.

· Enhance Kubernetes and Ray-based ML workloads for feature computation.

· Improve log aggregation and monitoring solutions for knowledge graph operations.

· Optimize large-scale ML workflows for personalization and semantic search.

 

Basic Qualifications:

 

· 4+ years of experience in DevOps, Site Reliability Engineering (SRE), or Cloud Infrastructure Engineering, as well as strong knowledge of Google Cloud Platform (GCP), AWS, or Azure.

· Demonstrated experience with online inferencing, expertise in TensorFlow model training and serving, and with high-performance parallel computing architectures.

· Experience with knowledge graph processing and large-scale embeddings.

· Strong experience with CI/CD tools such as GitHub Actions, Jenkins, or GitLab CI, as well as Kubernetes and container orchestration and expertise in infrastructure as code (IaC) using Terraform or Helm.

 

Additional Qualifications:

 

· Experience with message queues and event-driven architectures (Pub/Sub, Kafka, etc.).

· Proficiency in monitoring and logging solutions (Datadog, Prometheus, OpenTelemetry, etc.).

· Strong scripting skills in Python, Bash, or Go for automation.

· Experience with Graph Neural Networks (GNNs) and large-scale knowledge graphs.

· Hands-on experience with ML model serving frameworks (TensorFlow Serving, Triton, TorchServe, etc.).

· Familiarity with load balancing, API gateways, and caching strategies.

· Experience optimizing low-latency microservices for ML-based personalization.

· Understanding of distributed training strategies and large-scale feature computation.

· Passion for building and maintaining high-performance infrastructure for ML-based personalization.

 

What We Offer:

 

· A culture of learning focused on innovative ML infrastructure and DevOps standard processes.

· A collaborative team environment where engineering supports real-time personalization.

· A remote-friendly work setup with opportunities to work on scalable knowledge graph and asset embedding systems.

This role is a great opportunity to shape the future of ML infrastructure for feature engineering, knowledge graphs, and semantic search by building efficient, scalable, and high-performance systems.

 

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