Senior AI/ML Platform Engineer (LLM/SLM Inference)

The application window is expected to close on: 06/26/2026Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received. Meet the Team Join Cisco’s CX AI Incubation Team as a Senior AI/ML DevOps Engineer and help productionize LLM/SLM capabilities for Intelligent Customer Experiences, across cloud and on-prem environments. In Cisco CX, you will build and operate scalable AI systems that move from prototype to production, powering delivery intelligence, network automation, infrastructure testing, and intelligence on edge. You will collaborate with product and engineering teams to deploy reliable, secure, and observable AI services, optimizing inference performance from CPU and small GPUs to large multi-GPU servers, including air-gapped and customer-managed deployments. Your Impact Join Cisco’s Customer Experience (CX) AI Incubation team to build and run production-grade AI platforms and services that transform customer engagement and operational efficiency. You will focus on end-to-end AI DevOps for LLMs/SLMs, including on-prem inference packaging, runtime optimization, deployment automation, and model/service observability. This role requires strong software engineering, hands-on GPU inference experience, and a track record of operationalizing models at scale. What You’ll Do • Productionize LLM/SLM-powered features by building robust model-serving and deployment pipelines (cloud + on-prem) with clear SLAs, monitoring, and rollback strategies. • Optimize inference performance across CPU, small GPUs, and large multi-GPU servers using quantization, batching, KV-cache strategies, and runtime tuning for cost and latency. • Package and integrate on-prem inference stacks (VM/containers) with customer environments, including secure configuration, versioning, and upgrade-safe deployments. • Design scalable serving architectures for generative AI (multi-tenant, secure, cost-aware), including capacity planning and performance benchmarking. • Build automated CI/CD for models and prompts: evaluation gates, regression testing, artifact management, and reproducible releases. • Implement model and service observability: latency/throughput metrics, quality drift signals, safety checks, and incident triage workflows. • Support training and fine-tuning workflows for LLMs/SLMs, including data curation, experiment tracking, and packaging models for production. • Partner with product and engineering to integrate AI services into applications, ensuring reliability, security, and responsible AI behavior.