Position Details
About this role
Lead architect and technical authority for a scalable ML inference platform within Prisma AIRS, driving MLOps and production-grade AI solutions at scale for security applications.
Key Responsibilities
- Architect and design scalable ML inference platform
- Provide technical leadership and mentorship
- Drive model and system performance optimization
- Set engineering standards for automated model deployment and monitoring
- Collaborate with cross-functional teams to ensure end-to-end system cohesion
Technical Overview
Stack includes Python/Java/C++, Kubernetes, Docker, TensorFlow, PyTorch, ONNX, TensorRT, vLLM/SGLang, CUDA, Kafka/Spark/Flink; cloud: GCP/AWS/Azure/OCI; CI/CD with Jenkins, GitLab CI, Tekton; focus on distributed, low-latency ML inference and model deployment.
Ideal Candidate
The ideal candidate is a senior ML platform engineer with deep expertise in ML systems, MLOps, and scalable inference, strong cloud experience (GCP/AWS/Azure), and a track record architecting distributed ML services.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Clearance & Visa
Keywords for Your Resume
Deal Breakers
No BS/MS/PhD in CS/EE or equivalent, No cloud experience on GCP or lack of Kubernetes/Docker experience, No experience with ML frameworks (TensorFlow, PyTorch)
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