Principal AI Enablement Engineer

Verisign•Reston, VA

20h•$180,700 - $244,500•Hybrid

About The Position

Verisign is building a new engineering team to establish and operate the company’s on-premises Artificial Intelligence and Large Language Model (LLM) platform. The Principal AI Enablement Engineer will serve as the platform’s senior technical authority and help define how AI capabilities are safely, reliably, and effectively implemented throughout the company. This hands-on technical leadership position combines systems and infrastructure engineering with practical knowledge of modern LLMs, inference platforms, developer tooling, and AI-assisted enterprise workflows. The role will substantially influence platform architecture, technology selection, engineering standards, operational practices, and long-term technical direction, while supporting Verisign’s Protect / Grow / Manage framework. Security and governance are central to the position. The engineer will help technical teams adopt AI in ways that protect company data, comply with approved use policies, and support responsible AI practices. This includes understanding how AI tools interact with sensitive information, controlling access to AI capabilities, and enabling safe use of AI in engineering workflows in coordination with governance, security, and platform stakeholders. The position is based in Verisign’s Reston, Virginia office and has a hybrid work schedule. The pay range is $180,700–$244,500 in annual base salary. Actual base pay may vary based on job-related knowledge, skills, and experience. A discretionary bonus based on individual and company performance is offered, and certain roles may be eligible for discretionary stock awards.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Telecommunications, or a related field, or equivalent experience.
15+ years of 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 or scripting ability in Python, Go, C/C++, or equivalent languages.
Practical experience deploying LLMs.
Understanding of LLM inference concepts, including context windows, tokenization, quantization, batching, concurrency, memory requirements, and accelerator utilization.
Ability to lead major technical initiatives without requiring direct personnel management authority.
Excellent written and verbal communication skills.
Strong security mindset and ability to work closely with governance, security, and platform stakeholders.
Ability to understand how AI tools interact with sensitive information, how access to AI capabilities should be controlled, and how technical users can safely incorporate AI into engineering workflows.

Nice To Haves

Experience in one or more of the following areas is desirable; expertise in every area is not expected.
GPU or accelerator-based computing.
LLM inference engines such as llama.cpp, vLLM, Ollama, TensorRT-LLM, or similar technologies.
Kubernetes or other container orchestration platforms.
Retrieval-Augmented Generation (RAG), embeddings, vector search, and enterprise knowledge retrieval.
Agentic AI systems, tool-calling models, coding agents, or Model Context Protocol (MCP).
AI-assisted software development environments and workflows.
Model evaluation, benchmarking, and automated evaluation frameworks.
Open-weight LLM deployment and model lifecycle management.
Enterprise authentication, authorization, secrets management, and audit systems.
Experience working in environments with strict security, privacy, reliability, or regulatory requirements.

Responsibilities

Serve as the principal technical authority for the company’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 to assess model quality, performance, reliability, and suitability for company workloads.
Work with Information Security teams to design secure mechanisms for applying LLMs to proprietary source code, internal data, documentation, and analytical workloads.
Work with engineering teams to develop appropriate AI-assisted software engineering capabilities, including coding agents, tool use, repository integration, retrieval systems, and related developer workflows.
Enhance engineering practices for model deployment, upgrades, rollback, observability, capacity planning, and incident response.
Work with security and infrastructure teams to establish appropriate controls for sensitive corporate information.
Evaluate emerging AI technologies and distinguish production-suitable technologies from experimental or immature approaches.
Prototype new capabilities and convert successful experiments into reliable production services.
Mentor engineers on the AI platform team and provide technical leadership across organizational boundaries.
Work directly with internal users to identify high-value AI applications, including software development, data analysis, automation, and knowledge retrieval.
Help technical teams adopt AI capabilities in ways that protect company data, align with approved use policies, and support responsible AI practices.
Work closely with governance, security, and platform stakeholders on AI security and adoption.