Director, AI Engineering

Position Overview

The Director of AI Engineering will lead the development and implementation of enterprise-wide artificial intelligence capabilities, establishing the technical foundation, architecture, and engineering practices needed to deliver scalable AI agents and machine learning solutions. This role will oversee AI initiatives across cloud-based platforms and secure, on-premises infrastructure, ensuring that solutions are reliable, secure, and aligned with organizational objectives.

The Director will build and manage a high-performing engineering organization, recruit experienced technical professionals, and establish the platforms and processes required to move AI initiatives from experimentation to production. Working in close partnership with Data Engineering, Data Science, IT, cybersecurity, and business leaders, this individual will identify opportunities to apply AI to complex operational and commercial challenges, delivering measurable business value through automation, advanced analytics, and improved decision-making.

This is a hands-on technical leadership position requiring both strategic vision and practical engineering expertise. The successful candidate will guide architectural design, contribute directly to early-stage development and prototyping, establish technical standards, and foster a culture of innovation, accountability, and continuous improvement. A strong emphasis will be placed on secure-by-design development, responsible AI adoption, and the protection of sensitive enterprise data.

The ideal candidate combines deep knowledge of AI and machine learning technologies with sound architectural judgment, proven team leadership, strong business acumen, and the ability to deliver results in a dynamic, technically complex environment.

This is a full-time, in-office position based at the company's headquarters in Arlington, Virginia. The Director will report to the Vice President of Software Engineering and Enterprise Analytics.

General Description: Duties and Responsibilities

• Develop and implement the enterprise AI engineering strategy, defining the technology roadmap, architectural direction, and delivery model to support business objectives.
• Establish and lead a multidisciplinary engineering team encompassing AI engineering, machine learning, platform engineering, and applied AI development.
• Design and implement secure, scalable AI infrastructure that supports development, testing, deployment, and operation across cloud and on-premises environments.
• Lead the adoption and optimization of self-hosted, open-weight large language models (LLMs), including model evaluation, fine-tuning, quantization, inference optimization, and serving architecture. Determine when commercial model APIs provide the most effective solution.
• Define architectural patterns, development standards, and reusable components for agentic AI systems capable of processing batch and streaming data to address operational and commercial requirements.
• Collaborate with Data Engineering to develop enterprise ontologies, semantic models, and data integration strategies that enable AI applications to access and interpret trusted business information.
• Direct the evaluation and acquisition of GPU computing resources and related infrastructure, including vendor selection, procurement, capacity forecasting, cost management, and resource utilization.
• Work with business and operational leaders to identify, assess, and prioritize AI opportunities based on feasibility, expected return, strategic importance, and implementation complexity.
• Lead AI initiatives from initial discovery and proof of concept through production deployment, adoption, performance measurement, and ongoing improvement.
• Evaluate and implement custom machine learning models where appropriate, selecting the most effective combination of traditional ML, generative AI, and agent-based approaches for each business problem.
• Establish and maintain MLOps and LLMOps capabilities, including automated deployment pipelines, model lifecycle management, testing, observability, performance monitoring, governance, and technical documentation.
• Partner with IT, cybersecurity, and relevant governance teams to ensure AI platforms and applications meet applicable security requirements, access controls, regulatory obligations, and industry standards.
• Communicate engineering progress, resource requirements, delivery risks, and measurable business outcomes to executive leadership.
• Promote engineering excellence through technical mentorship, knowledge sharing, consistent development practices, and continuous evaluation of emerging AI technologies.

Qualifications

• 10 or more years of experience in software engineering, data engineering, machine learning, or related technical disciplines, including at least 3 years of experience leading technical teams.
• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
• Demonstrated success designing, developing, and operating production-grade AI and machine learning systems in enterprise environments.
• Practical experience deploying and managing large language models, particularly open-weight models hosted on private infrastructure, with knowledge of quantization, inference optimization, GPU resource management, and model-serving technologies.
• Experience architecting agentic AI applications, including multi-step workflows, tool integration, orchestration, and enterprise system connectivity.
• Strong programming and data-processing skills, particularly in Python and Apache Spark, along with familiarity with contemporary AI and machine learning frameworks.
• Experience designing and operating AI workloads across public cloud environments and on-premises GPU infrastructure.
• Proven ability to recruit, develop, and retain high-performing engineering teams while establishing clear technical direction and delivery accountability.
• Experience evaluating infrastructure requirements, managing technology vendors, planning capacity, and overseeing technical procurement.
• Excellent communication and collaboration skills, with the ability to explain complex technical concepts and recommendations to executive leadership, business partners, and technical teams.
• Strong organizational and problem-solving skills, with the ability to manage competing priorities, meet delivery commitments, and adapt to evolving business and technical requirements.
• Demonstrated commitment to secure engineering practices, operational reliability, and responsible deployment of AI technologies.

Preferred Qualifications

• Master's degree or doctorate in Computer Science, Engineering, Mathematics, Data Science, or another quantitative or technical discipline.
• Experience with Databricks, distributed computing, and real-time or streaming data architectures.
• Background delivering AI and machine learning solutions in industrial, operational technology (OT), infrastructure, or safety-sensitive environments.
• Knowledge of ontology development, knowledge representation, semantic modeling, and enterprise data architecture.
• Familiarity with agent orchestration frameworks such as LangGraph, CrewAI, or comparable custom-built solutions.
• Industry experience in energy, liquefied natural gas (LNG), manufacturing, utilities, or other asset-intensive sectors.
• Experience implementing DevOps, MLOps, and LLMOps workflows, including CI/CD automation, model evaluation, deployment management, and production observability.
• Strong technical writing skills, including the ability to document system architectures, engineering standards, operational procedures, and technical recommendations.