Principal AI Enablement Engineer

Verisign•Reston, VA

3h•$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 serves as the platform's senior technical authority, helping define how AI capabilities are safely, reliably, and effectively implemented throughout the company. This is a hands-on technical leadership position combining systems and infrastructure engineering with practical knowledge of modern LLMs, inference platforms, developer tooling, and enterprise AI-assisted workflows. The role has substantial influence over platform architecture, technology selection, engineering standards, operational practices, and long-term technical direction, while supporting Verisign's Protect / Grow / Manage framework. The engineer will work with security, infrastructure, governance, platform stakeholders, engineering teams, and internal users to identify and deliver valuable AI applications while protecting sensitive company information and promoting responsible AI practices. This position is based in Verisign's Reston, Virginia office and has a hybrid work schedule. The stated pay range is $180,700–$244,500 annually. Base pay may vary based on job-related knowledge, skills, and experience. The company offers a discretionary bonus based on individual and company performance, 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 an equivalent language.
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 direct personnel management authority.
Excellent written and verbal communication skills.
Strong security mindset and ability to work closely with governance, security, and platform stakeholders.

Nice To Haves

Experience with GPU or accelerator-based computing.
Experience with LLM inference engines such as llama.cpp, vLLM, Ollama, TensorRT-LLM, or similar technologies.
Experience with Kubernetes or other container orchestration platforms.
Experience with Retrieval-Augmented Generation (RAG), embeddings, vector search, and enterprise knowledge retrieval.
Experience with agentic AI systems, tool-calling models, coding agents, or Model Context Protocol (MCP).
Experience with AI-assisted software development environments and workflows.
Experience with model evaluation, benchmarking, and automated evaluation frameworks.
Experience with open-weight LLM deployment and model lifecycle management.
Experience with 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 ways to apply 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 turn successful experiments into reliable production services.
Mentor other 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, follow approved use policies, and support responsible AI practices.
Work with governance, security, and platform stakeholders on access controls and safe incorporation of AI into engineering workflows.