Director, AI Engineering Located: Arlington Summary The Director of AI Engineering is responsible for building and leading Venture Global's AI Engineering function. This leader will define the strategy, architecture, and operating model for deploying AI agents and machine learning systems across the enterprise, spanning cloud and secure on-premises environments. The Director will stand up the platform, hire and lead a team of senior AI engineers and machine learning engineers, and partner closely with Data Engineering, Data Science, and business stakeholders to translate operational and commercial needs into deployed, revenue-generating and insight-driving AI solutions. This is a hands-on leadership role: the Director is expected to personally drive architecture decisions, build early prototypes, and set engineering standards. The Director will champion a safety-conscious, security-first posture in all AI deployments. The measures of an ideal candidate include technical depth, architectural judgment, people leadership, stakeholder communication, strategic thinking, and a strong bias for delivery. This new position will be based in our Arlington, VA headquarters and report to the Vice President of Software Engineering and Enterprise Analytics. The position is full-time in office located in Arlington, VA. General Description Duties & 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. Qualifications - 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. Preferred Qualifications - 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. Salary Range $221,000 - $260,000