Title Senior DevOps Engineer

Paramount Pictures; New York (NY); Remote; USD 98,000 - 170,000; Full time

Job summary

An innovative company is looking for a Senior DevOps Engineer to join their Personalization Team. This role focuses on developing core backend infrastructure for high-performance computing, particularly in knowledge graph-based recommendations and asset embedding systems. You will design scalable infrastructure, optimize Kubernetes deployments, and automate CI/CD pipelines for machine learning models. This position offers a collaborative environment with a culture of learning, allowing you to shape the future of ML infrastructure while working on exciting projects in a remote-friendly setup. Join us to make a significant impact in the field of machine learning!

Benefits

Medical Insurance

Dental Insurance

Vision Insurance

401(k)

Tuition Assistance

Paid Time Off

Bonus Eligible

Qualifications

• 4+ years in DevOps or Cloud Infrastructure Engineering with strong GCP, AWS, or Azure knowledge.
• Experience with online inferencing and high-performance parallel computing.

Responsibilities

• Design and manage scalable infrastructure for knowledge graph and asset embedding pipelines.
• Optimize Kubernetes deployments for ML feature engineering and real-time inference.

Skills

DevOps

Cloud Infrastructure Engineering

Kubernetes

TensorFlow

CI/CD

Python

Bash

Go

Knowledge Graph Processing

Large-scale ML Workloads

Tools

GitHub Actions

Jenkins

GitLab CI

Terraform

Helm

Prometheus

Datadog

OpenTelemetry

Ray

Kafka

Job description

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Job Title: Senior DevOps Engineer - Knowledge Graph & Asset Embedding Systems

We are seeking a Senior DevOps Engineer to join our Personalization Team. The role involves developing core backend infrastructure for personalization, feature engineering, and search, with a focus on high-performance computing for knowledge graph-based recommendations and asset embedding systems. Ideal candidates will have experience with Kubernetes, Ray Clusters, TensorFlow, Prometheus, and large-scale ML workloads.

Responsibilities Include:

1. Design, implement, and manage scalable infrastructure for knowledge graph and asset embedding pipelines.
2. Optimize Kubernetes deployments for ML feature engineering and real-time inference.
3. Develop and maintain Ray Clusters for distributed ML workloads.
4. Automate CI/CD pipelines for deploying ML models and services.
5. Implement monitoring solutions using tools like Prometheus, Datadog, and OpenTelemetry.
6. Ensure high availability, security, and performance of ML pipelines.
7. Collaborate with ML engineers to deploy and scale personalization services.
8. Optimize infrastructure for TensorFlow model training and serving.
9. Implement autoscaling strategies for ML feature computation.
10. Debug and resolve production issues related to latency, scaling, and reliability.

Key Projects:

• Build scalable feature engineering pipelines for personalization and search.
• Develop high-performance knowledge graph processing infrastructure.
• Enhance Kubernetes and Ray-based ML workloads.
• Improve monitoring and log aggregation for knowledge graph operations.
• Optimize large-scale ML workflows for personalization and semantic search.

Basic Qualifications:

• 4+ years of experience in DevOps, SRE, or Cloud Infrastructure Engineering with strong GCP, AWS, or Azure knowledge.
• Experience with online inferencing, TensorFlow training and serving, and high-performance parallel computing.
• Experience with knowledge graph processing and large-scale embeddings.
• Strong skills with CI/CD tools (GitHub Actions, Jenkins, GitLab CI), Kubernetes, and infrastructure as code (Terraform, Helm).

Additional Qualifications:

• Experience with message queues (Kafka, Pub/Sub).
• Proficiency in monitoring/logging solutions (Datadog, Prometheus, OpenTelemetry).
• Scripting skills in Python, Bash, or Go.
• Experience with Graph Neural Networks and large knowledge graphs.
• Hands-on experience with ML serving frameworks (TensorFlow Serving, Triton, TorchServe).
• Knowledge of load balancing, API gateways, caching strategies.
• Experience optimizing low-latency microservices for ML personalization.
• Understanding distributed training and large-scale feature computation.
• Passion for building high-performance ML infrastructure.

What We Offer:

• A culture of learning focused on ML infrastructure and DevOps.
• Collaborative environment supporting real-time personalization.
• Remote-friendly work setup on scalable knowledge graph systems.

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

About Paramount+

Paramount+ is a streaming service from Paramount Global, featuring live sports, news, and entertainment from brands like BET, CBS, MTV, Nickelodeon, and more. It offers a vast library of original series, movies, and exclusive sports programming, including live CBS stations and other channels like CBSN, CBS Sports HQ, and ET Live.

Additional Information

Hiring Salary Range: $98,400 - $170,000. Compensation depends on location, experience, and other factors. Benefits include medical, dental, vision, 401(k), insurance, tuition assistance, and PTO. The position is bonus eligible.

Equal Opportunity Statement

Paramount is an equal opportunity employer committed to diversity and inclusion. Reasonable accommodations are available for qualified individuals with disabilities or disabled veterans. Contact: 212.846.5500 or [e-mail].