Sr. Software Engineer Cloud infrastructure

45960

Burbank, CA, US, 91505

Technology

Burbank

Full-Time

On-Site

#We Are Paramount  on a mission to unleash the power of content… you in? We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness.

Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

The Applied Intelligence Data Engineering team is seeking a Senior Software Engineer – Cloud Infrastructure. This is a hybrid role that blends deep software engineering with hands‑on ownership of cloud infrastructure. It is purpose‑built for engineers who are equally writing production Java services and designing multi‑cloud platform architecture.

In this role, you will own the full lifecycle of cloud‑native systems. You will build high‑performance streaming applications and architect the infrastructure they run on. You will also ensure they are production‑grade, observable, and secure. You will bridge application and platform engineering, reducing the gap between what software teams need and what the cloud platform delivers.

This role requires deep expertise in distributed systems, cloud‑native architecture, Kubernetes, and software engineering best practices. You should have a track record of operating at both the application and infrastructure layers.

Optimize Data Streaming Applications on Cloud

• Own end‑to‑end performance of data streaming applications running on cloud infrastructure — from Kafka topic configuration through consumer processing and downstream delivery.
• Profile and tune streaming pipelines to maximize throughput and minimize latency, leveraging cloud‑native compute, storage, and networking resources.
• Identify and address bottlenecks at the intersection of application code and cloud resource constraints, including CPU throttling, network saturation, I/O limits, and memory constraints.
• Design and implement cloud resource utilization strategies. This includes spot/preemptible instances, managed streaming services, and dynamic node pool scaling to balance performance with cost efficiency.
• Benchmark streaming pipelines end‑to‑end and translate findings into actionable infrastructure and code improvements.
• Collaborate with Data and AI/ML engineering teams to ensure streaming pipelines are optimally provisioned for real‑time feature engineering, inference, and analytics workloads.

• Develop high‑throughput, low‑latency streaming applications using Java and Kafka.
• Design event‑driven microservices that process, enrich, and route real‑time data at scale.
• Implement reactive, non‑blocking architectures to support high concurrency and resilience.
• Develop reusable streaming frameworks, libraries, and platform capabilities to improve engineering velocity and standardization.
• Architect, implement, and optimize multi‑cloud infrastructure across GCP and OCI to support large‑scale data and streaming workloads.
• Design and implement advanced networking architectures, including VPC peering, VPNs, load balancers, and cross‑region failover strategies.
• Build and maintain Terraform‑based infrastructure‑as‑code frameworks to standardize deployments and enable developer self‑service.
• Define autoscaling, deployment, failover, and resource optimization strategies for high‑volume production systems.
• Contribute to platform‑wide architecture decisions related to scalability, resiliency, high availability, and disaster recovery.
• Deploy and manage containerized microservices within Kubernetes environments (GKE, OKE) across cloud platforms.
• Implement container orchestration best practices, service‑mesh configurations, and rolling‑deployment strategies.
• Partner with platform engineering teams to improve developer tooling, deployment automation, and runtime reliability.

• Ensure production‑grade reliability, observability, and…