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# Machine Learning Engineering Technical Leader - CX AI

- **Company:** Cisco Systems, Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Salary:** $256,900.0 - $333,400.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Inference Optimization, OpenAI, Tensorflow, Management of Software Versions, Privacy Controls, Pytorch, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Generative AI, Agentic-AI, AI Platforms, SGLang, Machine Learning Operations, TensorRT, Drift Detection, Api Management, Cisco
- **Published:** October 26, 2026
- **Apply:** https://www.sanjosejobsite.com/job.asp?id=3467044840&tx=VT6257TTZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 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., 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.

## Description

As 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., * 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.