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Llm Training Jobs in Virginia (NOW HIRING)

LLM Specialist

Mclean, VA

$16.50 - $21.75/hr

The LLM Specialist serves as PenFed's subject matter expert for Large Language Models (LLMs ... Support AI literacy efforts and enterprise training initiatives. * Serve as a trusted advisor and ...

AI Systems Engineer

Chantilly, VA · On-site

$152K - $190K/yr

Exceptional ability to translate highly technical AI concepts-such as LLM training methodologies, RAG architecture, model alignment, and corpus curation-into strategic, actionable business ...

Exceptional ability to translate highly technical AI concepts-such as LLM training methodologies, RAG architecture, model alignment, and corpus curation-into strategic, actionable business ...

The AI Security & LLM Engineer will design, secure, and optimize our next-generation generative AI ... Tuition and training reimbursement * Life and AD&D Insurance About AnaVation AnaVation is the ...

The AI Security & LLM Engineer willdesign, secure, and optimize our next-generation generative AI ... Tuition and training reimbursement * Life and AD&D Insurance About AnaVation AnaVation is the ...

The AI Security & LLM Engineer will design, secure, and optimize our next-generation generative AI ... Tuition and training reimbursement * Life and AD&D Insurance About AnaVation AnaVation is the ...

AI Security & LLM Engineer

Reston, VA · On-site

$170 - $250/hr

Description of Task to be Performed The AI Security & LLM Engineer will design, secure, and ... Tuition and training reimbursement * Life and AD&D Insurance About AnaVation AnaVation is the ...

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Llm Training information

What is an LLM Training?

An LLM Training job involves developing, fine-tuning, and optimizing large language models (LLMs) to improve their performance and accuracy. This role typically includes data collection, preprocessing, model training, evaluation, and troubleshooting issues related to bias, efficiency, and scalability. Professionals in this field work with machine learning frameworks, large datasets, and computational resources to enhance AI capabilities. They may also collaborate with researchers, engineers, and product teams to deploy models for real-world applications.

What are the key skills and qualifications needed to thrive in the LLM Training position?

To excel in LLM Training, you need a strong background in machine learning, natural language processing (NLP), and computer science, often backed by an advanced degree in a related field. Experience with programming languages such as Python, frameworks like PyTorch or TensorFlow, and familiarity with data annotation tools are essential, along with knowledge of cloud platforms and distributed computing. Strong analytical thinking, effective communication, and the ability to collaborate across interdisciplinary teams set top candidates apart. These skills ensure high-quality model development, efficient project execution, and the ability to adapt to evolving AI technologies.

What types of teams or professionals does an LLM Training specialist typically collaborate with?

Professionals specializing in LLM Training often work closely with data engineers, software developers, domain experts, product managers, and quality assurance analysts. Collaboration is essential for collecting and preprocessing training data, integrating models into products, and ensuring outputs meet business and user requirements. These roles frequently participate in agile project workflows, contribute to cross-functional team meetings, and collaborate on continuous model improvements. Engaging with diverse teams expands your understanding of product goals and helps you deliver robust and reliable language models that align with organizational objectives.

Is it possible to train Llm Training?

Training large language models (LLMs) is possible but requires significant computational resources, expertise in machine learning, and access to large datasets. It typically involves using specialized hardware like GPUs or TPUs and knowledge of frameworks such as TensorFlow or PyTorch. Many organizations opt to fine-tune pre-trained models rather than train from scratch due to the high costs and complexity involved.

What are the most commonly searched types of Llm Training jobs in Virginia?

The most popular types of Llm Training jobs in Virginia are:

What are popular job titles related to Llm Training jobs in Virginia?

For Llm Training jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Llm Training jobs in Virginia look for?

The top searched job categories for Llm Training jobs in Virginia are:

What cities in Virginia are hiring for Llm Training jobs?

Cities in Virginia with the most Llm Training job openings:

Infographic showing various Llm Training job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 15% Part Time, and 2% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution.

PenFed Credit Union
Finance and Insurance • 1 - 5K employees

7.6

Company rating: 7.6 out of 10

Based on 14 frontline employees who took The Breakroom Quiz

Good employer

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$16.50 - $21.75/hr

Full-time

Posted 29 days ago


Job description

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

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

Education:UNAVAILABLEEmployment Type: FULL_TIME


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