The application window is expected to close on: 11/26/2026
This is a Hybrid role requiring 3 days per week in the San Jose, CA office.
Meet the TeamCisco's CX AI Foundations team owns the foundational AI platform behind intelligent customer experiences across Cisco - the shared layer that application and agent teams across CX build on, in cloud, on-premises, and fully air-gapped customer environments.
Our charter spans the platform end to end: the model and inference layer (including SLM specialization and the LLM-compatible API), evaluation systems for shipped AI behavior, agentic and orchestration infrastructure, foundational AI services and APIs, and the packaging and upgrade machinery that makes AI supportable in the field - including the on-prem AI appliance now shipping to customers. The portfolio is expanding as Cisco's AI footprint grows.
This is a small, deeply technical team with production commitments.
Your ImpactAs a senior hands-on technical leader in Cisco's CX Engineering organization, you will architect, build, and deliver foundational, enterprise-scale AI services that power critical platform capabilities. Operating as a builder rather than a coordinator, you will own complex technical challenges end-to-end-driving model selection and fine-tuning, inference optimization, agentic infrastructure, multi-tenant API contracts, and air-gapped deployments across diverse compute targets. You will establish the benchmark for scientific rigor and statistical evaluation-treating regression gating, judge calibration, and drift detection as mandatory release criteria-while mentoring senior engineers through technical depth and influence rather than positional authority. In this role, you will bridge cutting-edge AI research with reliable production engineering, collaborating across product, security, and executive leadership to drive architectural roadmaps, uphold responsible AI governance, and champion engineering excellence across the organization.
Minimum Qualifications:- Bachelor's degree with 11+ years of related experience, or Master's degree with 7+ years of related experience.
- Machine learning experience to include model development, training and adaptation, and evaluation.
- Experience with Python and modern ML frameworks such as PyTorch or JAX or similar.
- Experience taking machine learning work from research or prototype through to production.
- Production experience in at least one foundational AI platform area - model serving and inference, evaluation systems for generative AI, agentic/orchestration infrastructure, or AI platform services and APIs.
- Experience leading full lifecycle projects.
Preferred Qualifications:Technical & Architectural Leadership- Production Platform Ownership: Track record architecting and operating shared AI/ML inference platforms and APIs consumed across multiple teams, including API contract design, versioning, and backward compatibility.
- Incubation to Delivery: Proven experience leading concurrent technical workstreams and navigating AI solutions from experimental incubation through to supported, enterprise-grade production products.
- Engineering Mentorship: Demonstrated success mentoring, coaching, and elevating senior engineers and applied researchers.
Applied Machine Learning & Inference Systems Depth- High-Performance Inference: Hands-on experience deploying and profiling LLM/SLM serving engines (vLLM, TensorRT-LLM, Triton, SGLang, llama.cpp) utilizing optimizations such as continuous batching, KV-cache management, quantization, and speculative decoding.
- Model Specialization & Adaptation: Deep expertise in fine-tuning, distillation, transfer learning, and PEFT/LoRA, as well as designing OpenAI-compatible interfaces over specialized models.
- Rigorous Generative Evaluation: Experience building statistical evaluation frameworks for non-deterministic AI systems-including golden datasets, LLM-as-a-judge calibration, human-agreement metrics, regression gates, and drift detection.
- Agentic Architectures: Production experience designing multi-turn agentic workflows, autonomous tool integration, and advanced retrieval (RAG) architectures.
Enterprise, Edge & MLOps Infrastructure- Hybrid & Air-Gapped Deployments: Experience packaging and delivering AI services into cloud, customer-managed, air-gapped, and resource-constrained compute environments (CPU, NPU, small GPU) with strict upgrade safety and supportability.
- Production MLOps: Hands-on foundation in modern AI infrastructure practices, including automated CI/CD pipelines for models, model registries, experiment tracking, and real-time telemetry/observability.
- Security & Governance: Comprehensive understanding of enterprise security reviews, data boundary isolation, privacy controls, and responsible AI compliance.
Thought Leadership & External Influence- Industry Impact: Record of external technical contributions via patents, peer-reviewed publications, open-source AI projects, or conference presentations.
