Principal, AI Engineer
About The Position
The Principal AI Engineer is the senior-most individual contributor and technical authority within Venture Global's AI Engineering function. This role owns the reference architecture, design patterns, and engineering standards for how the company builds and deploys AI agents and machine learning systems across cloud and secure on-premises environments. The Principal AI Engineer works hands-on, prototyping, building, and hardening the most complex and highest-risk components, while setting the technical direction that the broader team executes against. This role partners closely with the Director of AI Engineering on strategy, with Data Engineering on ontology and semantic modeling, and with senior stakeholders to ensure architectural decisions are sound, scalable, and secure. The Principal AI Engineer will define safety-first, human-in-the-loop design patterns for any system interfacing with operational technology. The measures of an ideal candidate include deep technical mastery, architectural judgment, influence without authority, mentorship of senior engineers, and a strong bias for delivery. This new position will be based in our Arlington, VA headquarters and report to the Director of AI Engineering. The position is full-time in office located in Arlington, VA.
Requirements
- 10+ years of experience in software, data, or machine learning engineering, with a track record of technical leadership as a senior individual contributor.
- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field of study.
- Deep, hands-on expertise designing and deploying production AI/ML systems at scale.
- Advanced experience with large language models, including self-hosting open-weight models, fine-tuning, quantization, and serving/inference optimization.
- Demonstrated experience architecting agentic AI systems and the orchestration patterns that support them.
- Expert-level proficiency in Python and modern AI/ML tooling and frameworks.
- Strong experience across cloud platforms and on-premises GPU infrastructure.
- Proven ability to set technical direction and influence engineering decisions without direct authority.
- Experience mentoring senior engineers and elevating the technical bar of a team.
- Excellent communication skills, with the ability to articulate complex technical trade-offs to both engineers and non-technical stakeholders.
- Strong work ethic with the ability to effectively prioritize, meet deadlines, adapt to changing priorities, and succeed in a fast-paced environment.
Nice To Haves
- Advanced degree in a quantitative or technical discipline.
- Experience with Databricks and streaming data platforms.
- Experience deploying AI/ML solutions in or adjacent to operational technology (OT), industrial, or safety-critical environments.
- Deep familiarity with agent frameworks (e.g., LangGraph, CrewAI, or custom orchestration).
- Experience with ontology and semantic modeling approaches.
- Experience developing custom machine learning models (neural networks and smaller models) in addition to LLM-based systems.
- Experience in the energy, LNG, manufacturing, or heavy industrial sectors.
- Experience utilizing DevOps/MLOps/LLMOps practices and tooling.
- Strong technical writing skills.
Responsibilities
- Define and own the reference architecture for AI agent development and deployment across cloud and on-premises environments.
- Establish engineering standards, design patterns, and best practices for agentic systems, model serving, and AI/ML integration.
- Lead the technical design and hands-on implementation of the most complex, high-risk, or foundational components of the AI platform.
- Architect self-hosted open-weight LLM solutions, including model selection, fine-tuning strategies, quantization, and inference/serving optimization for both batch and streaming workloads.
- Design scalable agent architectures capable of analyzing enterprise data to drive operational efficiency, commercial value, and insight discovery.
- Partner with Data Engineering to ensure AI systems are grounded in well-designed ontology and semantic models.
- Define safety-first, human-in-the-loop patterns and appropriate guardrails for AI systems.
- Evaluate and select AI frameworks, tooling, and infrastructure; prototype and validate emerging approaches before adoption.
- Provide deep technical mentorship to senior AI engineers, platform engineers, applied AI engineers, and machine learning engineers.
- Establish standards for model governance, monitoring, evaluation, and documentation to ensure maintainability and reliability.
- Contribute to infrastructure and procurement decisions by defining technical requirements for GPU servers and AI infrastructure.
- Serve as the primary technical escalation point for the most challenging engineering problems across the team.
- Contribute to strategic discussions and represent AI Engineering in cross-functional architecture and design reviews.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
