Software Engineer, AI Engineer (Applied​/Software), AI Reliability​/ Performance Engineer

The application window is expected to close on: 10/29/2026

We run the platform that serves foundational models to Cisco IT. Our Foundational Model Service, gives engineering teams across the company access to small, large, and embedding models. youWe serve those models on Kubernetes clusters, with Nim, Vllm and other runtimes. Beyond serving, We benchmark, evaluate, monitor, and release new models as improvements and demand warrant. We use published model artifacts where possible, refining or rebuilding them for compatibility, performance, or quality.

Our customers depend on the platform under a 99.9% uptime SLA, and we build and operate accordingly.

As a Senior Software Engineer, you'll guide model serving, runtime tuning, accelerator optimization, and release evaluation. You'll lead incident response and improve reliability, performance, and operability. You'll also use agentic workflows to identify problems earlier and automate remediation.

You'll follow Cisco Design Thinking Principles, simplify user experience, apply secure coding practices, and protect privacy. You'll work across design, product, and engineering to improve customer solutions, documentation, development practices, and production reliability.

This role calls for production experience in AI infrastructure, model serving, evaluation, and related services. We're looking for a self starter who works independently, turns ambiguous problems into plans, mentors engineers, and raises technical standards. The platform evolves with new runtimes, accelerators, and models. On call is shared across the team.

• Contribute to model serving direction and roadmap, including runtime selection and tuning across vLLM, NVIDIA NIM, and other runtimes.
• Guide quantization and accelerator optimization across GPU vendors, validating performance, quality, capacity, and cost with data
• Develop and enhance platform services, APIs, gateways, and operational tooling around model inference
• Evolve evaluation, benchmarking, and load test frameworks that gate model releases against service level objectives
• Define model promotion criteria across quality, safety, latency, throughput, and resource use
• Evaluate how fine tuning, distillation, and quantization affect production behavior
• Shape routing and capacity behavior, including prefix caching, KV aware routing, and prefill and decode separation
• Improve model registry, packaging, evaluation, release, and development workflows using Infrastructure as Code, Git Hub Actions, and agentic workflows
• Refine model artifacts when runtime compatibility or performance requires changes
• Develop observability that shows platform health, model performance, capacity, customer adoption, and usage
• Monitor production, serve as an escalation point for on call issues, lead postmortems and root cause analyses, and drive durable improvements
• Coordinate across customer, product, design, and engineering teams to gather input, forecast capacity, track milestones, and guide platform direction
• Apply AI to platform operations through anomaly detection, automated remediation, and predictive operations
• Lead features and projects from technical design through completion, working with minimal guidance and driving results through delegation and review
• Write clean code and unit tests independently, and review code for quality, threat models, scale, reliability, and release velocity
• Act as a technical resource, mentor engineers, run design reviews, and share knowledge across teams
• Create technical designs, runbooks, user documentation, project updates, and remediation plans

• 7 or more years of related systems, platform, or software engineering experience, or equivalent practical experience, with solid knowledge across related technologies
• A production background developing and operating AI infrastructure
• A solid understanding of LLM, SLM, embedding, and reranker model internals, including context length, batching, token throughput, and memory use
• Hands on work serving models on inference runtimes including vLLM, NVIDIA NIM, or Triton
• Proven results building services around models, including the APIs, gateways, and operational tooling that make them consumable
• Depth in evaluating and benchmarking models, and using the results to make release decisions
• Familiarity moving model artifacts through evaluation, optimization, packaging, and production serving
• Deep understanding of supervised fine tuning, parameter efficient fine tuning, distillation, and quantization
• Command of…