Principal AI Enablement Engineer
Verisign · Reston,Virginia,United States
About this role
**Verisign — Principal AI Enablement Engineer** Verisign helps enable the security, stability, and resiliency of the internet. We’re building a new engineering team responsible for establishing and operating the company’s on-premises Artificial Intelligence and Large Language Model (LLM) platform. This is a **hands-on technical leadership** role. You’ll serve as the senior technical authority for the platform and help define how AI capabilities are implemented safely, reliably, and effectively across the company. --- ### Key Responsibilities - Serve as the principal technical authority for Verisign’s on-premises AI and LLM architecture - Design, implement, operate, and evolve production LLM inference platforms - Evaluate and recommend models, inference engines, developer tools, agent frameworks, and supporting technologies - Develop repeatable evaluation methodologies for model quality, performance, reliability, and suitability - Partner with Information Security to design secure mechanisms for applying LLMs to proprietary source code, internal data, documentation, and analytical workloads - Work with engineering teams to build AI-assisted software engineering capabilities (coding agents, tool use, repo integration, retrieval systems, and developer workflows) - Enhance engineering practices for model deployment, upgrades, rollback, observability, capacity planning, and incident response - Collaborate with security and infrastructure teams to establish controls for sensitive corporate information - Evaluate emerging AI technologies and distinguish production-ready solutions from experimental approaches - Prototype new capabilities and convert successful experiments into reliable production services - Mentor engineers and provide technical leadership across organizational boundaries - Work with internal users to identify high-value AI applications (software development, data analysis, automation, knowledge retrieval) --- ### Required Qualifications - Bachelor’s degree in Computer Science, Information Technology, Telecommunications, or related field **or equivalent experience** - **15+ years** computer engineering experience - Extensive experience designing, operating, or troubleshooting complex production systems - Strong Linux systems engineering experience - Strong understanding of distributed systems, networking, storage, containers, and modern infrastructure platforms - Experience with modern software development practices, source control, CI/CD, automation, and infrastructure-as-code - Strong programming/scripting ability (e.g., **Python, Go, C/C++** or equivalent) - Practical experience deploying LLMs - Understanding of LLM inference concepts (context windows, tokenization, quantization, batching, concurrency, memory requirements, accelerator utilization) - Ability to lead major technical initiatives without direct personnel management authority - Excellent written and verbal communication skills --- ### Preferred Qualifications - Experience in some of the following areas (expertise in all not required): - GPU/accelerator-based computing - LLM inference engines (e.g., **llama.cpp, vLLM, Ollama, TensorRT-LLM**) - Kubernetes or other container orchestration platforms - Retrieval-Augmented Generation (RAG), embeddings, vector search, enterprise knowledge retrieval - Agentic AI systems, tool-calling models, coding agents, or **Model Context Protocol (MCP)** - AI-assisted software development environments and workflow
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