Director, AI Engineering
About The Position
Venture Global LNG is seeking a Director of AI Engineering to build and lead the company's AI Engineering function. This role involves defining the strategy, architecture, and operating model for deploying AI agents and machine learning systems across both cloud and secure on-premises environments. The Director will establish the AI platform, recruit and manage a team of senior AI and machine learning engineers, and collaborate with Data Engineering, Data Science, and business stakeholders to transform operational and commercial needs into deployed AI solutions that generate revenue and insights. This is a hands-on leadership position requiring the Director to drive architectural decisions, develop early prototypes, and set engineering standards, while maintaining a safety-conscious and security-first approach to all AI deployments. Key attributes for an ideal candidate include technical depth, architectural judgment, people leadership, stakeholder communication, strategic thinking, and a strong focus on delivery. The position is full-time and based at the company's headquarters in Arlington, VA, reporting to the Vice President of Software Engineering and Enterprise Analytics.
Requirements
- 10+ years of experience in software, data, or machine learning engineering, including 3+ years leading technical teams.
- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field of study.
- Demonstrated experience architecting and deploying production AI/ML systems at enterprise scale.
- Hands-on experience with large language models, including self-hosting open-weight models and serving optimization (e.g., quantization, inference optimization, GPU utilization).
- Experience designing and deploying agentic AI systems and the frameworks/patterns that support them.
- Strong proficiency in Spark, Python and modern AI/ML tooling.
- Experience building on cloud platforms and on-premises GPU infrastructure.
- Proven ability to hire, lead, and mentor high-performing engineering teams.
- Experience with infrastructure procurement, capacity planning, and vendor management.
- Excellent interpersonal and communication skills, with the ability to translate technical concepts for executive and business audiences.
- 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.
- Experience with ontology and semantic modeling approaches.
- Familiarity with agent frameworks (e.g., LangGraph, CrewAI, or custom orchestration).
- Experience in the energy, LNG, manufacturing, or heavy industrial sectors.
- Experience utilizing DevOps/MLOps practices and tooling.
- Strong technical writing skills.
Responsibilities
- Define and execute the AI engineering strategy, roadmap, and operating model for the enterprise, aligned to business and operational priorities.
- Build, lead, and mentor a team of senior AI engineers, platform engineers, applied AI engineers, and machine learning engineers.
- Architect scalable, secure infrastructure for AI agent development and deployment across cloud and on-premises environments.
- Lead the strategy for self-hosted open-weight large language models (LLMs), including model selection, fine-tuning, quantization, and serving optimization, while integrating commercial API-based models where appropriate.
- Establish the reference architecture and engineering standards for agentic systems that analyze both batch and streaming data for operational and commercial use cases.
- Partner with Data Engineering on ontology and semantic model design to ground AI systems in trusted enterprise data.
- Oversee procurement of GPU servers and AI infrastructure, managing vendor relationships, capacity planning, and budget.
- Partner with business unit leaders to identify and prioritize high-value AI use cases and drive them from concept to production.
- Explore and enable development of custom machine learning models alongside LLM-based approaches.
- Establish MLOps/LLMOps practices, model governance, monitoring, and documentation standards for maintainability and reliability.
- Collaborate with IT and security leadership to operate within energy-sector security and governance frameworks.
- Report on team performance, project outcomes, and business impact to executive leadership.
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What This Job Offers
Job Type
Full-time
Career Level
Director
