Distinguished Machine Learning Engineer,AISWP Hybrid - Cisco Job San Jose area,California USA,Software Development

Define and implement multi-year technical strategies for agentic systems, ensuring unified architectural alignment across Cisco’s broad product portfolio and global engineering organizations.
Serve as the primary internal and external technical authority, negotiating critical architectural decisions with senior executives and driving the adoption of industry standards, such as the Model Context Protocol (MCP).
Architect complex systems that use hybrid and graph-based retrieval, creating unique, scalable solutions for cross-product telemetry, root cause analysis, and automated remediation.
Establish the company-wide standards for AI safety, observability, and compliance, ensuring that all agent-based autonomous actions remain measurable, auditable, and resilient to production risks.
Cultivate a high-performance engineering culture by mentoring Principal and Senior-level leaders, fostering innovation, and representing Cisco’s technical excellence at industry forums, standards bodies, and global conferences.
Bachelor’s degree in computer science or a related field with 17+ years of total software engineering experience;
Master's with 14+ years; or PhD with 10+ years.
5+ years of experience operating at a"Principa" level or above, with documented proof of influencing technical strategy across at least 3 distinct product organizations.
2+ years of experience in the design, development, and production-level deployment of LLM-based systems serving at least 10,000+ daily active users or processing 500+ requests per second.
Successfully architected and shipped at least 1 production-grade multi-agent system using frameworks such as Lang Graph, Temporal, or an equivalent complex state-machine stack.
10+ years of deep experience writing production-grade code in Go, Python, or Rust, specifically within hyperscale environments (e.g., managing architectures supporting 100+ microservices or petabyte-scale data pipelines).
Experience designing and implementing at least 3 critical safety or observability features in a production environment (e.g., indirect prompt injection defense, human-in-the-loop gates, or automated rollbacks).
Experience establishing AI governance frameworks (e.g., NIST AI RMF, EU AI Act) and ensuring enterprise compliance across the model lifecycle.
Expertise in advanced post-training methodologies, including reinforcement learning from verifiable rewards (RLHF/DPO) and supervised fine-tuning.
Deep knowledge of inference serving optimizations, such as KV/prefix caching, continuous batching, and model routing strategies.
Contributions to the external technical community, including published research, patents, or active leadership roles in industry open-source standards bodies.

#J-18808-Ljbffr

Distinguished Machine Learning Engineer, AISWP Hybrid - Cisco