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About This Role
Job Overview
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.
Responsibilities
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.
Qualifications
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\.00
This 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
Benefits
At PenFed, we offer a robust benefits package designed to support you both personally and professionally. You'll have access to comprehensive health, dental, and vision plans; paid time off; and family\-friendly benefits like paid parental leave, care support, and fitness center access. Financial wellness is encouraged through features like a 401(k) match, employee loan discounts, and fully paid life and disability coverage. We also support growth via education assistance, community involvement, and volunteer opportunities.
Our Purpose
Helping members achieve their dreams since 1935\. Pentagon Federal Credit Union (PenFed) is one of America's largest federal credit unions, serving 2\.8 million members worldwide with $29 billion in assets. PenFed offers market\-leading certificates, checking and savings, credit cards, personal loans, mortgages, auto loans, and a wide range of other financial services, always with members' interests in mind. PenFed is federally insured by the NCUA and is an Equal Housing Lender.
Berkshire Hathaway HomeServices PenFed Realty, LLC is a full\-service real estate company ready to assist our clients with buying, selling and renting a home. The company is a wholly owned subsidiary of PenFed Credit Union and is the largest independently\-owned brokerage in the Berkshire Hathaway HomeServices network, placing us in the top 1% of all real estate brokerages in the country. With almost 60 offices and nearly 2,000 world\-class sales professionals, we offer complete service coverage in Virginia, Maryland, the District of Columbia, Delaware, Pennsylvania, West Virginia, Florida, Tennessee, Kansas and Texas. In addition, we also offer specialized client services which include management of vacation properties and long\-term rentals, corporate relocation services and national referral network.
Equal Employment Opportunity
PenFed management will maintain and observe personnel policies which will not discriminate or permit harassment or retaliation against a person because of race, color, creed, age, sex, gender, gender identity, gender expression, religion, national origin, ancestry, marital status, military or veteran status or obligation, the presence of a physical and/or mental disability or medical condition, genetic information, sexual orientation, and all statuses protected by applicable state or local law in all recruiting, hiring, training, compensation, overtime, position classifications, work assignments, facilities, promotions, transfers, employee treatment, and in all other terms and conditions of employment. PenFed will also prohibit retaliation against individuals for raising a complaint of discrimination or harassment or participating in an investigation of same. PenFed will also reasonably accommodate qualified individuals with a disability so that they can apply for a job or perform the essential functions of a job unless doing so causes a direct threat to these individuals or others in the workplace and the threat cannot be eliminated by reasonable accommodation or if the accommodation creates an undue hardship to PenFed. Contact human resources (HR) with any questions or requests for accommodation at [email protected] .
Salary Context
This $79K-$161K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PenFed Credit Union, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($120K) sits 44% below the category median. Disclosed range: $79K to $161K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
PenFed Credit Union AI Hiring
PenFed Credit Union has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $161K - $440K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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