Senior Applied AI Engineer
About The Position
The Senior Applied AI Engineer is responsible for turning business and operational needs into deployed AI solutions. This role works directly with business units to understand how they operate, identifies where AI agents can generate revenue, drive efficiency, or surface insights, and then architects, builds, and deploys those agents into production. The Senior Applied AI Engineer sits at the intersection of the business and the AI platform, partnering closely with Data Engineers for data and integration needs and with Data Scientists for machine learning model integrations, while building on the infrastructure and standards established by the AI Engineering team. This is a hands-on delivery role that pairs strong engineering skills with the business acumen and communication needed to translate ambiguous problems into working solutions. Given the proximity of these solutions to LNG operations, the Senior Applied AI Engineer will apply a safety-first, human-in-the-loop approach. The measures of an ideal candidate include technical proficiency, business acumen, stakeholder communication, delivery focus, collaboration, and a willingness to learn the domain. This new position will be based in our Arlington, VA headquarters and report to the Director of AI Engineering.
Requirements
- 5+ years of experience in software, machine learning, or AI engineering, including delivering solutions in partnership with business stakeholders.
- Bachelor's degree in Computer Science, Engineering, Mathematics, Data Analytics, or related field of study.
- Hands-on experience building and deploying AI/ML or LLM-based applications in production.
- Experience developing agentic AI solutions, including prompting, tool use, retrieval-augmented generation (RAG), and orchestration.
- Strong proficiency in Python and modern AI/ML tooling.
- Demonstrated ability to translate business problems into technical solutions and deliver measurable outcomes.
- Experience integrating with enterprise data sources and machine learning models.
- Strong presentation skills to effectively communicate solutions and results to stakeholders.
- Excellent interpersonal and communication skills, with strong critical thinking and attention to detail.
- 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
- Experience with agent frameworks (e.g., LangGraph, CrewAI, or custom orchestration).
- Experience with self-hosted open-weight LLMs and API-based commercial models.
- Experience with Databricks and streaming data platforms.
- Experience working with streaming datasets/IoT or operational data.
- Experience applying AI/ML solutions in the energy, LNG, manufacturing, or heavy industrial sectors.
- Familiarity with ontology and semantic models.
- Experience in agile development and sprint planning.
- Strong technical writing skills.
Responsibilities
- Partner with business and operational stakeholders to understand their workflows, challenges, and objectives, and identify high-value opportunities to apply AI agents.
- Translate business requirements into technical solution designs for agentic AI applications.
- Build, deploy, and iterate on AI agents that analyze batch and streaming data to generate revenue, improve operational efficiency, or discover insights.
- Collaborate with Data Engineers to define data requirements and integrate agents with enterprise data sources, ontology, and semantic models.
- Collaborate with Data Scientists to integrate machine learning models into agentic solutions.
- Develop prompts, tools, retrieval strategies, and orchestration logic to make agents accurate, reliable, and grounded in trusted data.
- Design and implement evaluation approaches to measure agent quality, accuracy, and business impact.
- Apply safety-first, human-in-the-loop design for solutions that interface with or near operational technology (OT).
- Actively engage in business stakeholder requirement workshops to understand, interpret, and translate requirements into effective technical solutions.
- Communicate solution options, trade-offs, and outcomes clearly to both technical and business audiences.
- Build on the platform, frameworks, and standards established by the AI Engineering team, providing feedback to improve reusability and scalability.
- Document solutions, workflows, and design decisions for maintainability and knowledge sharing.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
