Efficiency AI Ops Engineer Hybrid

The application window is expected to close on: 10/01/2026

** This role is hybrid. Onsite 3 days per week in Raleigh (Research Triangle Park), North Carolina or San Jose, California*
* ** Meet the Team*
* We are the "Circuit" platform team, the engine powering AI-driven business transformation for 80,000+ Cisco users. We operate at the intersection of applied research and production. We are a rapidly expanding, high-impact group dedicated to building the vehicle for AI-driven business transformation. We aren't building prototypes; we are building the infrastructure that defines how Cisco runs its business on AI.

** Your Impact*
* As an Efficiency AI Ops Engineer, you are a hands-on technical lead who treats AI as an engineering problem. You will not just "use" AI-you will architect, optimize, and scale it. You will tackle high-impact problems like LLM token efficiency, automated model routing, and batch capacity management. In addition, you will introduce new Agentic capabilities to bring business applications to Circuit in AI first manner.

Your success is measured by the tangible performance, new capabilities and cost-optimization of the Circuit platform. This is an opportunity to work on a high-visibility platform where your contributions to product innovation and development processes have a tangible, global impact on how Cisco runs its business.

** Responsibilities*
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** Production Optimization:
** Architect and deploy systems for real-time AI workload management, including model routing and latency reduction.

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** Cost-Efficiency Engineering:
** Research and implement strategies for token compression, batch capacity, and fine-tuning to maximize the return on investment of our model garden.

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** Production Reliability:
** Build automated regression and validation pipelines for AI agents to ensure code generated by AI is production-ready, secure, and reliable.

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** Technical Leadership:
** Act as a "Team Captain" for specific features; drive consensus among multi-functional teams and mentor peers to improve our collective engineering rigor.

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** System Ownership:
** Take full ownership of a feature from research and design through to production deployment, security, monitoring, and post-mortem analysis.

** Minimum Qualifications*
* + Bachelor's degree + 6 years of related experience (or Master's + 4, PhD +
1) in Computer Science or a highly technical field.

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** Demonstrated experience in a production environment:
** Must show evidence of deploying and maintaining scalable software systems, not just standalone POCs.

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** Deep hands-on expertise with AI/ML systems:
** Experience with model fine-tuning, RAG, or agentic frameworks where you have had to evaluate trade-offs between cost, quality, and performance.

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** Strong Software Engineering foundation:
** Ability to write clean, maintainable code, lead code reviews, and implement automated testing (regression/CI/CD).

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** Consistent track record of technical ownership:
** Evidence of leading projects from initial concept to deployment and managing the production lifecycle.

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** Strong critical thinking
** skills with the ability to evaluate and document technical trade-offs regarding cost, quality, and performance.

** Preferred Qualifications*
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Experience with real-time AI workload management and custom LLM fine-tuning to meet defined business metrics.

+ Expertise in using AI-assisted coding tools (e.g., Cursor, Git Hub Copilot) to accelerate development while enforcing strict security standards.

+ Good communication and influence:
Ability to translate complex technical trade-offs to product management and non-technical partners.

+ Experience building and productizing agentic applications with focus on evaluation and business acceptance.

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