Principal AI Engineer

VERISIGN; Reston (VA); On-site; USD 180,000 - 260,000; Full time

Job summary

VERISIGN is building a new engineering team to establish and operate the company's on-premises AI and LLM platform. The Principal Engineer will serve as the senior technical authority for the platform and define how AI capabilities are safely, reliably, and effectively implemented across the company.

This is a hands-on technical leadership role. The candidate will combine systems and infrastructure engineering with knowledge of modern LLMs, inference platforms, and enterprise AI workflows.

Qualifications

• Bachelor's degree in CS/IT or equivalent experience.
• 15+ years computer engineering experience.
• Strong understanding of distributed systems, networking, storage, containers and modern infra platforms.
• Experience with modern software dev practices, source control, CI/CD, automation, and IaC.
• Proficient in Python, Go, C/C++ or equivalent.
• Understanding of LLM inference concepts (context windows, tokenization, batching, memory, accelerators).
• Ability to lead major technical initiatives without direct personnel management.

Responsibilities

• Serve as the principal technical authority for the company's on-prem AI/LLM architecture.
• Design, implement, operate, and evolve production LLM inference platforms.
• Evaluate models, inference engines, tools, and companion technologies.
• Develop evaluation methodologies for model quality, performance, and reliability.
• Collaborate with Information Security to secure LLM usage on proprietary data.
• Lead AI-assisted software engineering capabilities and developer workflows.
• Improve practices around deployment, upgrades, observability, and capacity planning.
• Coordinate with security and infrastructure teams for data controls.
• Assess emerging AI technologies for production readiness.
• Prototype capabilities and translate experiments into production services.
• Mentor engineers and provide cross-team technical leadership.
• Work with internal users to identify high-value AI applications.

Skills

Distributed systems

CI/CD

Automation

Python/Go/C/C++

Infrastructure as code

Technical leadership

Communication

Education

Bachelor's degree (CS/IT or related)

Tools

Kubernetes

CI/CD tooling

Job description

We are building a new engineering team responsible for establishing and operating the company's on-premises Artificial Intelligence and Large Language Model (LLM) platform. The Principal Engineer will serve as the senior technical authority for the platform and will help define how AI capabilities are safely, reliably, and effectively implemented throughout the company.

This is a hands-on technical leadership position. The successful candidate will combine strong systems and infrastructure engineering skills with practical knowledge of modern LLMs, inference platforms, developer tooling, and AI-assisted workflows in an enterprise environment.

The Principal Engineer will have substantial influence over architecture, technology selection, engineering standards, operational practices, and the long-term technical direction of the platform. This must be accomplished while continuing the Protect / Grow / Manage framework that guides the company.

Key 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 for determining 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
• Develop and work with engineering teams to create the most appropriate AI-assisted software engineering capabilities. This includes coding agents, tool use, repository integration, retrieval systems, and related developer workflows
• Enhance existing engineering practices and update them to perform, model deployment, upgrades, rollback, observability, capacity planning and incident response
• Work closely with security and infrastructure teams to establish appropriate controls for sensitive corporate information
• Evaluate emerging AI technologies and distinguish technologies suitable for production use from experimental or immature approaches
• Prototype new capabilities and convert 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 applications for AI, including software development, data analysis, automation, and knowledge retrieval

Required Qualifications:

• Bachelor's degree in Computer Science, Information Technology, Telecommunications, or related field or equivalent experience
• 15+ years computer 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 languages such as Python, Go, C/C++ or equivalent
• 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

Preferred Qualifications:

• Experience in several 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