Staff DevOps Engineer

Responsibilities As a Staff DevOps Engineer, you will architect, develop, and scale the core services, real-time data pipelines, and distributed infrastructure that power our cutting-edge AI products. You will bridge the gap between complex AI models and production-ready systems, ensuring that our high-throughput, real-time data streaming environment is reliable, secure, and infinitely scalable Architect and maintain highly available backend systems, distributed data pipelines, and scalable cloud infrastructure to support real-time AI applications Leverage infrastructure-as-code (IaC) and cloud-native tooling to build, manage, and optimize our production, staging, and development environments Build and manage stream-processing jobs capable of handling global, high-frequency data streams with ultra-low latency Work cross-functionally with AI Research, Engineering, and Data Science teams to containerize, deploy, orchestrate, and monitor scalable ML models and infrastructure Own the full development lifecycle—from system design and infrastructure provisioning to deployment, proactive monitoring, and capacity planning Maximize system performance, observability, and reliability; respond to high-priority production issues with advanced debugging, root cause analysis, and blameless post-mortems Mentor and guide team members, fostering an engineering culture centered around DevOps practices, automation, and distributed systems best practices Requirements Hands‑on experience with modern GitOps practices (e.g., Argo CD) and managing CI/CD automation pipelines Solid experience with containerization and orchestration (Docker, Kubernetes), including configuring network policies, storage, and auto‑scaling Strong proficiency in Java, Python and/or Rust, with a solid grasp of writing high‑performance, concurrent backend code Production experience with stream‑processing frameworks, specifically Apache Flink BS/MS/PhD degree in Computer Science, Software Engineering, or a related technical field Able to scale yourself with AI coding assistants while still fully understanding and taking absolute ownership of all code and infrastructure configurations you commit 5+ years of professional software engineering experience with a heavy focus on backend systems, distributed architectures, and infrastructure management Deep experience with cloud platforms (AWS, GCP, or Azure) and infrastructure‑as‑code tools such as OpenTofu/Terraform and Ansible Solid experience with relational databases (e.g., PostgreSQL), in‑memory datastores (e.g., Valkey/Redis), and high‑throughput data storage strategies Experience setting up observability stacks (e.g., Grafana, Prometheus, ELK, etc.) Excellent communication skills, with a proven ability to design resilient architectures and explain complex infrastructure paradigms to the broader team Deep experience with Apache Pulsar (or Kafka) handling high‑throughput, event‑driven streaming architectures Rust experience with async runtimes (Tokio) and message‑driven architectures Experience architecting real‑time media pipelines using WebRTC (LiveKit, RealtimeKit) with a focus on server‑side jitter buffering and network impairment handling Deep understanding of audio codecs, specifically optimizing server‑side Opus encoding/decoding for CPU efficiency and low‑latency distribution Familiarity with MLOps frameworks, GPU orchestration in Kubernetes, and serving LLMs at scale Background in high‑availability, real‑time systems, telecommunications, or safety‑critical networks Prior experience or a deep interest in aerospace, aviation, or autonomous tracking systems #J-18808-Ljbffr Archer