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Generative Ai Engineer Intern Jobs in Virginia (NOW HIRING)

Merican

AWS AI Engineer

Herndon, VA · On-site

Onsite About the Role We are seeking a highly skilled AWS AI Engineer with strong hands-on experience in Kubernetes, EKS, and Generative AI systems. The ideal candidate will have deep expertise in ...

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site +1

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne-tuning) * Hands-on exp with OCR, ICR and OMR technologies is a must * Good programming ...

Node.Digital LLC

Senior AI/ML Engineer

Herndon, VA · On-site

$140 - $190/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne‑tuning) * Hands‑on exp with OCR, ICR and OMR technologies is a must * Good programming ...

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne-tuning) * Hands-on exp with OCR, ICR and OMR technologies is a must * Good programming ...

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne-tuning) * Hands-on exp with OCR, ICR and OMR technologies is a must * Good programming ...

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne-tuning) * Hands-on exp with OCR, ICR and OMR technologies is a must * Good programming ...

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All JobsGenerative Ai Engineer Intern JobsGenerative Ai Engineer Intern Jobs in Virginia

Generative Ai Engineer Intern information

What does a generative AI engineer intern do?

A Generative AI Engineer Intern assists in developing and testing machine learning models, specifically those that can create new content such as text, images, or audio. They work with frameworks like TensorFlow or PyTorch, collaborate with senior engineers, and help improve the performance and reliability of generative AI systems. Interns may also be involved in data preprocessing, model evaluation, and keeping up with the latest research in artificial intelligence.

What skills and qualifications are needed to thrive as a generative AI engineer intern?

To thrive as a Generative AI Engineer Intern, you need a solid understanding of machine learning fundamentals, programming skills (especially in Python), and coursework or experience in artificial intelligence or computer science. Familiarity with deep learning frameworks like TensorFlow or PyTorch and version control systems such as Git is typically required, and relevant coursework or certifications in AI/ML are advantageous. Strong problem-solving skills, curiosity, and the ability to communicate complex ideas clearly help interns stand out. These skills and qualities are crucial for quickly learning advanced AI techniques, contributing to team projects, and driving innovation in a rapidly evolving field.

What types of projects can a generative AI engineer intern expect to work on during their internship?

As a Generative AI Engineer Intern, you can expect to work on projects involving the development, training, and evaluation of generative models such as GANs, VAEs, or transformer-based architectures. Typical tasks may include data preprocessing, model implementation, fine-tuning, and running experiments to improve model performance. Interns often collaborate closely with data scientists, software engineers, and research teams, gaining exposure to both research and application of AI in real-world products. This role provides hands-on experience with state-of-the-art tools and frameworks, offering a valuable foundation for a future career in AI engineering or research.

What is the difference between Generative Ai Engineer Intern vs Machine Learning Engineer Intern?

AspectGenerative Ai Engineer InternMachine Learning Engineer Intern
Required CredentialsBasic knowledge of AI, programming, and some coursework in machine learning or AIStrong foundation in machine learning, programming, and data analysis, often with coursework or certifications
Work EnvironmentTech companies, startups, research labs focusing on AI applicationsTech firms, research institutions, and companies applying machine learning models
Industry UsageDeveloping generative models like GPT, DALL·E, and similar AI toolsBuilding predictive models, data pipelines, and machine learning algorithms

While both roles involve AI and machine learning, a Generative Ai Engineer Intern focuses specifically on creating generative models like text, images, or audio, whereas a Machine Learning Engineer Intern works broadly on developing and deploying various machine learning algorithms across different applications.

What are the most commonly searched types of Generative Ai Engineer jobs in Virginia?

The most popular types of Generative Ai Engineer jobs in Virginia are:

What are popular job titles related to Generative Ai Engineer Intern jobs in Virginia?

For Generative Ai Engineer Intern jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Generative Ai Engineer Intern jobs in Virginia look for?

The top searched job categories for Generative Ai Engineer Intern jobs in Virginia are:

What cities in Virginia are hiring for Generative Ai Engineer Intern jobs?

Cities in Virginia with the most Generative Ai Engineer Intern job openings:

Generative Ai Engineer Intern jobs near you

Senior MLOps & Generative AI Engineer - Remote

Sentara Healthcare

Virginia Beach, VA • On-site, Remote

$99K - $136K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 15 days ago

Sentara Health rating

6.7

Company rating: 6.7 out of 10

Based on 412 frontline employees who took The Breakroom Quiz

531st of 888 rated healthcare providers

Job description

City/State
Virginia Beach, VA
Work Shift
Multiple shifts available
Overview:
Sentara is hiring a Senior MLOps & Generative AI Engineer!
This position is fully remote!
Selected candidates would be required to be onsite for final round of team interview
Candidates must reside in one of the following states:
Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Washington, West Virginia, Wisconsin, or Wyoming.
Overview
We are seeking a highly skilled and experienced Senior MLOps & Generative AI Engineer to join our growing AI organization and help advance current and future initiatives applying machine learning, deep learning, NLP, and Generative AI technologies to improve healthcare outcomes and operational excellence.
This role combines two critical focus areas:
  • MLOps Engineering - building and scaling enterprise-grade ML infrastructure, deployment pipelines, observability, governance, and automation capabilities.
  • Generative AI Engineering - designing, architecting, deploying, and optimizing secure, production-ready GenAI applications and platforms leveraging LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks.

As a Senior Engineer, you will partner closely with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and Generative AI solutions at enterprise scale. You will play a key role in shaping the organization's AI platform strategy, driving best practices, and delivering scalable, secure, and reliable AI systems in production healthcare environments.
Key Responsibilities
MLOps Engineering Responsibilities
  • Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
  • Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
  • Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
  • Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
  • Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
  • Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
  • Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
  • Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
  • Identify gaps and improvement opportunities within the organization's ML platform ecosystem and architect scalable solutions to address them.
  • Support enterprise AI governance, compliance, auditability, and model risk management requirements.
  • Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.

Generative AI Engineering Responsibilities
  • Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
  • Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
  • Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
  • Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
  • Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
  • Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
  • Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
  • Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
  • Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
  • Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
  • Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
  • Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
  • Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
  • Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Required Qualifications
  • 5+ years of experience building and deploying production software, ML systems, or AI platforms.
  • 1+ years of hands-on experience building production Generative AI or LLM-based applications.
  • Strong programming skills in Python and experience with software engineering best practices.
  • Experience with major deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or equivalent.
  • Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.
  • Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies.
  • Experience deploying AI/ML systems in cloud environments including AWS, Azure, or GCP.
  • Strong understanding of APIs, distributed systems, microservices, and scalable backend architectures.
  • Experience with Kubernetes, containerization, orchestration, and cloud-native infrastructure.
  • Experience implementing CI/CD pipelines, infrastructure automation, and MLOps best practices.
  • Experience building monitoring, observability, and alerting solutions for ML and AI systems.
  • Strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations.
  • Experience designing secure, scalable, production-ready AI platforms and services.
  • Strong communication and collaboration skills with the ability to work across technical and business teams.

Preferred Qualifications
  • Previous experience implementing Generative AI and MLOps solutions within healthcare environments.
  • Experience working with EPIC or healthcare interoperability platforms.
  • Understanding of HIPAA, PHI handling, healthcare compliance, and responsible AI practices.
  • Experience with AI governance frameworks, LLM evaluation methodologies, and AI safety tooling.
  • Experience with GPU infrastructure optimization and scalable inference architectures.
  • Familiarity with multi-agent AI systems and autonomous workflows.
  • Experience with event-driven architectures, streaming pipelines, and real-time inference systems.
  • Exposure to model fine-tuning techniques including LoRA, PEFT, RLHF, or domain adaptation strategies.
  • Experience with enterprise AI platform architecture and internal developer platforms.
  • Prior experience mentoring engineers and leading technical initiatives.

Education
  • 5+ years of relevant experience with a degree (Required)

or
  • 7+ years of relevant experience without a degree (Required)
  • Experience in lieu of Bachelor's Degree.

Certification/Licensure
  • No specific certification or licensure requirements

Experience
  • 5 to 7 years of relevant experience

We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for Full Time employment is: 91,416.00 - 152,380.80. Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities.
Keywords: Talroo-IT, MLOps, Gen AI, LLM, AWS, Azure, GCP, AI/ML, Python, PyTorch, Hugging Face Transformers, TensorFlow, RAG, EPIC, HIPAA, AI Governance
Benefits: Caring For Your Family and Your Career
• Medical, Dental, Vision plans
• Adoption, Fertility and Surrogacy Reimbursement up to 10,000
• Paid Time Off and Sick Leave
• Paid Parental & Family Caregiver Leave
• Emergency Backup Care
• Long-Term, Short-Term Disability, and Critical Illness plans
• Life Insurance
• 401k/403B with Employer Match
• Tuition Assistance - 5,250/year and discounted educational opportunities through Guild Education
• Student Debt Pay Down - 10,000
• Pet Insurance
• Legal Resources Plan
• Colleagues have the opportunity to earn an annual discretionary bonus if established system and employee eligibility criteria is met.
Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30,000-member workforce. Diversity, inclusion, and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves.
In support of our mission "to improve health every day," this is a tobacco-free environment.
For positions that are available as remote work, Sentara Health employs associates in the following states:
Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.

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