Distinguished Machine Learning Engineer, AISWP Hybrid - Cisco

OpenTalent; San Jose (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

Cisco is seeking a senior technical leader to define long-term architectural strategy for agentic systems and to unify Cisco’s portfolio under shared architectural standards. The role drives MCP adoption, oversees cross-product telemetry, RCA, and automated remediation, and establishes company-wide AI safety and governance practices.

The candidate will mentor senior leaders, influence multiple product organizations, and represent Cisco at industry forums and standards bodies worldwide.

Qualifications

• Bachelor’s degree in computer science or a related field with 17+ years of total software engineering experience.
• Master’s degree with 14+ years; or PhD with 10+ years.
• 5+ years of experience operating at a Principal level or above, influencing technical strategy across at least 3 product organizations.
• 2+ years of experience in design, development, and production deployment of LLM-based systems serving 10,000+ daily users or 500+ req/sec.
• 10+ years of deep experience writing production-grade code in Go, Python, or Rust in hyperscale environments.
• Experience designing and implementing at least 3 critical safety or observability features in production environments.
• Experience establishing AI governance frameworks (e.g., NIST AI RMF, EU AI Act).
• Expertise in post-training methodologies like RLHF/DPO and supervised fine-tuning.
• Deep knowledge of inference serving optimizations (KV/prefix caching, batching, model routing).
• Contributions to external technical community via research, patents, or leadership in open-source standards bodies.

Responsibilities

• Define and implement multi-year technical strategies for agentic systems.
• Serve as primary technical authority negotiating architectural decisions with executives.
• Architect complex systems using hybrid and graph-based retrieval for telemetry and remediation.
• Establish company-wide AI safety, observability, and compliance standards.
• Mentor senior leaders and represent Cisco at industry forums and standards bodies.

Skills

Go

Python

Rust

LLM systems

AI governance

Observability

Distributed systems

Leadership

Education

Bachelors in CS

Masters in CS

PhD in CS

Tools

LangGraph

Temporal

Job description

• 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 LangGraph, 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.