Machine Learning Engineering Technical Leader - CX AI

Cisco•San Jose, CA

1d•$256,900 - $333,400•Hybrid

About The Position

Cisco'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. 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

Architect, build, and deliver foundational, enterprise-scale AI services that power critical platform capabilities.
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.
Establish the benchmark for scientific rigor and statistical evaluation—treating regression gating, judge calibration, and drift detection as mandatory release criteria.
Mentor senior engineers through technical depth and influence rather than positional authority.
Bridge cutting-edge AI research with reliable production engineering.
Collaborate across product, security, and executive leadership to drive architectural roadmaps.
Uphold responsible AI governance.
Champion engineering excellence across the organization.

Benefits

medical, dental and vision insurance
a 401(k) plan with a Cisco matching contribution
paid parental leave
short and long-term disability coverage
basic life insurance
10 paid holidays per full calendar year, plus 1 floating holiday for non-exempt employees
1 paid day off for employee’s birthday, paid year-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco
16 days of paid vacation time per full calendar year, accrued at rate of 4.92 hours per pay period for full-time employees (non-exempt)
flexible vacation time off program (exempt)
80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next
Additional paid time away may be requested to deal with critical or emergency issues for family members
Optional 10 paid days per full calendar year to volunteer
annual bonuses (for non-sales roles)
performance-based incentive pay (for sales roles)
Cisco restricted stock units