Principal Machine Learning Engineer

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Cisco's CX AI Incubation and Agents team is hiring a Principal Machine Learning Engineer to serve as the architectural and technical authority for the AI foundation powering Cisco IQ. The role spans classical ML, domain-specific language models, and Agentic AI, with hands-on design of production-scale AI systems across cloud, on-premises, and air-gapped environments. You will lead cross-functional engagements from architecture through customer deployment and set engineering standards for system design, platform governance, and cost efficiency.

• Design and build multi-tenant AI services with scalable APIs, versioning, backward compatibility, and quota management across cloud, on-premises, and air-gapped environments
• Select, evaluate, and implement models, frameworks, inference runtimes, and orchestration technologies for production architectures
• Drive AI cost optimization through token efficiency, model selection, inference performance, and compute utilization
• Lead architecture and design reviews, resolve complex technical trade-offs, and mentor senior engineers
• Influence platform direction through hands-on technical leadership and execution

• Bachelors + 14 years, Masters + 11 years, or PhD + 7 years of related experience
• Experience developing, fine-tuning, and optimizing models in Python, PyTorch or JAX using PEFT/LoRA, quantization, and distillation
• Experience architecting and deploying production LLM/SLM inference runtimes optimized with continuous batching, KV-cache management, or speculative decoding
• Experience architecting and deploying production RAG systems and agentic workflows using vector databases
• Experience designing automated evaluation frameworks including golden test datasets, LLM-as-a-judge, drift detection, and CI/CD regression gates
• Experience architecting production AI platform APIs with quota management, versioning, and hybrid cloud/air-gapped deployment

• Recognized industry contributions via publications, patents, conference presentations, or open-source AI/ML projects
• Hands-on experience optimizing inference for resource-constrained environments (CPU, NPU, small GPU footprints, on-prem appliances)