PenFed is hiring a (Hybrid) LLM Specialist at our Tysons, Virginia location. The LLM Specialist serves as PenFed s subject matter expert for Large Language Models (LLMs), Generative AI technologies, and emerging foundation models. This role is responsible for evaluating, selecting, implementing, securing, and optimizing LLM solutions that support business objectives, enhancing member experiences, improving operational efficiency, and drive employee productivity. The incumbent partners closely with business leaders, Technology, Information Security, Risk Management, Compliance, Data, and AI Engineering teams to translate business requirements into effective AI solutions. The LLM Specialist evaluates technical, security, performance, and cost considerations across AI models and platforms while ensuring LLM deployments align with enterprise AI management, responsible AI principles, privacy requirements, and regulatory expectations. This role serves as a trusted advisor on LLM technologies, helping the organization understand model capabilities, limitations, risks, and opportunities while supporting the successful adoption of Generative AI across the enterprise. Equivalent combination of education and experience is considered. Bachelor s degree in computer science, Data Science, Information Systems, Engineering, Artificial Intelligence, or a related field required. Advanced degree preferred. Minimum of 4 years of experience working directly with Generative AI, Large Language Models, Machine Learning, or related technologies. Experience evaluating and implementing solutions across multiple LLM platforms and model providers. Demonstrated experience supporting AI solution design, deployment, and operationalization. Experience communicating technical topics to business stakeholders and executive audiences. Financial services experience preferred. Strong understanding of Large Language Models, Generative AI technologies, foundation models, and emerging AI architectures. Knowledge of prompt engineering, retrieval-augmented generation (RAG), embeddings, vector databases, model evaluation, and fine-tuning approaches. Understanding of AI security risks, including prompt injection, adversarial attacks, data privacy concerns, access controls, and secure integration practices. Familiarity with AI governance, responsible AI principles, privacy requirements, and risk management considerations. Knowledge of model performance evaluation techniques, benchmarking methodologies, and LLMOps/MLOps practices. Ability to evaluate and communicate cost, performance, security, and scalability tradeoffs associated with AI solutions. Strong analytical, problem-solving, and critical thinking skills. Excellent verbal, written, presentation, and stakeholder management skills. Ability to translate complex technical concepts into clear business-oriented recommendations. Ability to work effectively across technical, business, compliance, risk, and leadership teams. Strong organizational skills with the ability to manage multiple priorities in a fast-paced environment. Supervisory Responsibility This position will not supervise employees. Licenses and Certifications There are no additional certifications required. Work Environment While performing the duties of this job, the employee is regularly exposed to an indoor office setting with moderate noise. •Most roles require working in an office setting with moderate noise and the ability to lift 25 pounds.• Travel Ability to travel to various worksites and be on call will be required. Pay Transparency The anticipated starting salary range for this role is $79,400.00 - $161,041.00This position is eligible for an organizational performance based annual bonus, subject to board discretion and approval.

This position is eligible for an individual performance based annual bonus. #LI-Hybrid Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. This is not intended to be an all-inclusive list of job duties, and the position will perform other duties as assigned. LLM Evaluation & Model Selection Evaluate and recommend Large Language Models (LLMs), foundation models, and Generative AI platforms based on business requirements, technical capabilities, security requirements, and operational considerations. Assess models across criteria including accuracy, latency, context window, performance, scalability, explainability, integration requirements, and business fit. Analyze and communicate technical tradeoffs among commercial, open source, hosted, and enterprise AI solutions. Provide recommendations regarding model architecture, deployment strategy, and solution design for business use cases. AI Solution Design & Enablement Partner with business units and technology teams to identify, define, and evaluate AI use cases. Translate business requirements into technical specifications and AI solution recommendations. Design and evaluate prompting, retrieval-augmented generation (RAG), fine-tuning, and model orchestration approaches when appropriate. Support implementation of AI-enabled applications, workflows, copilots, agents, and knowledge management solutions. Collaborate with AI Engineers and solution teams to ensure effective deployment and adoption of AI solutions. Cost Optimization & Value Realization Evaluate and communicate cost implications associated with AI platforms, models, and deployment approaches. Analyze token consumption, inference costs, hosting models, licensing structures, and operational expenses. Develop financial analyses and cost models that enable business stakeholders to make informed investment decisions. Recommend optimization strategies that balance business value, performance, scalability, and cost efficiency. Security, Privacy & Risk Management Assess security risks associated with LLM implementations, including prompt injection, data leakage, unauthorized access, model misuse, adversarial attacks, and other emerging threats. Collaborate with Information Security, Risk Management, Compliance, and Legal teams to ensure AI solutions operate within established enterprise standards and policies. Support the evaluation of AI solutions for privacy, security, and regulatory compliance requirements. Participate in risk identification, mitigation planning, monitoring activities, and remediation efforts related to AI technologies. Assist in establishing controls that support responsibility and secure AI deployment across the enterprise. Monitoring & Continuous Improvement Monitor AI model performance, effectiveness, reliability, and operational outcomes after deployment. Identify performance degradation, emerging vulnerabilities, and opportunities for improvement. Recommend model updates, technology enhancements, and alternative solutions as AI capabilities evolve. Maintain awareness of advancements in Generative AI, LLMs, model evaluation methodologies, and industry best practices. Education & Knowledge Sharing Develop guidance, standards, job aids, and documentation related to LLM usage and Generative AI best practices. Educate business and technical stakeholders on AI capabilities, limitations, risks, and responsible usage considerations. Support AI literacy efforts and enterprise training initiatives. Serve as a trusted advisor and technical resource for enterprise AI initiatives. Compliance Maintain knowledge of and ensure adherence to all applicable federal and state laws, regulations, and PenFed policies, procedures, and standards. Support compliance with enterprise AI management, information security, privacy, risk management, and data governance requirements. Assist with internal audits, regulatory examinations, and independent reviews related to AI technologies. Promote responsible AI practices consistently with PenFed's ethical, security, compliance, and risk management objectives.