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Ai Applications Engineer Jobs in Virginia (NOW HIRING)

Knexus

AI Software Engineer (Remote)

Vienna, VA · On-site +1

We're looking for an experienced AI Software Engineer with 6-8 years of experience to join our AI applications team. This is a fully remote position for candidates in the continental U.S., with work ...

cyberThink, Inc.

AI Engineer

Richmond, VA · On-site

Role: Senior Programmer Analyst - AI and Emerging Technologies Duration - Long Term contract ... Are you comfortable designing, coding, testing, and deploying applications in a multi-platform ...

Cornerstone Defense LLC

AI/ML Engineer

Herndon, VA · On-site

$100 - $140/hr

AI/ML Engineer Location: Herndon, Virginia Clearance: Active TS/SCI w/ Polygraph needed to apply ... applications and data centric mission management applications, with a focus on artificial ...

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site +1

$107K - $147K/yr

Senior AI/ML Engineer Location: Herndon, VA (Hybrid Work) Preferred: US Citizenship Node.Digital is ... You will focus on building generative AI applications with embedded artifcial intelligence or ...

Node.Digital LLC

Senior AI/ML Engineer

Herndon, VA · On-site

$140 - $190/hr

You will focus on building generative AI applications with embedded artifcial intelligence or ... UiPath RPA Developer Certification and UiPath AI Center Experience * Knowledge of chatbot ...

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Ai Applications Engineer information

What is an AI applications engineer?

AI Applications Engineers are professionals who design, develop, and integrate artificial intelligence (AI) solutions into software applications to solve real-world problems. They work closely with data scientists, software engineers, and business stakeholders to build and deploy machine learning models, automate processes, and enhance user experiences. Their responsibilities often include selecting appropriate AI technologies, writing code, testing models, and optimizing performance. AI Applications Engineers play a key role in translating AI research and prototypes into scalable and maintainable products used in industries like healthcare, finance, retail, and more.

What are the key skills and qualifications needed to thrive as an AI applications engineer?

To thrive as an AI Applications Engineer, you need strong programming abilities (Python, Java, or C++), a solid understanding of machine learning algorithms, and a relevant degree in computer science or engineering. Familiarity with AI frameworks (such as TensorFlow or PyTorch), cloud platforms, and data processing tools is typically required, along with certifications in machine learning or AI. Excellent problem-solving, collaboration, and communication skills help you translate business needs into effective AI solutions and work efficiently with cross-functional teams. These skills are critical for building scalable, reliable AI systems that deliver tangible value to organizations.

How does an AI applications engineer typically collaborate with data scientists and software developers on project teams?

As an AI Applications Engineer, you will often serve as a bridge between data scientists, who build and optimize machine learning models, and software developers, who integrate these models into production systems. Collaboration usually involves translating model requirements into scalable application features, ensuring model outputs align with user needs, and troubleshooting technical challenges that arise during deployment. Regular meetings, code reviews, and shared documentation are common practices to keep everyone aligned and ensure seamless integration. This cross-functional teamwork enhances both the technical robustness and usability of AI-powered applications.

What is the difference between Ai Applications Engineer vs Data Scientist?

AspectAi Applications EngineerData Scientist
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of AI/ML toolsBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops AI solutions, collaborates with engineering teamsAnalyzes data, builds models, interprets results
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, tech, research institutions

While both roles involve AI and data, Ai Applications Engineers focus on developing and deploying AI solutions in engineering contexts, whereas Data Scientists analyze data to extract insights. The roles often overlap but differ mainly in their primary focus and application environment.

What does an AI applications engineer do?

An AI applications engineer designs, develops, and implements artificial intelligence solutions to solve specific business problems. They work with machine learning models, data processing, and programming tools like Python or TensorFlow, often collaborating with data scientists and software developers to deploy AI systems effectively.

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Ai Applications Engineer jobs near you

Generative AI Engineer (Clearance Required)

InterImage

Arlington, VA • On-site

$60.75 - $83/hr

Full-time

Re-posted 13 days ago

Job description

InterImage is looking for engineers who thrive where innovation meets execution. Our Product Division rapidly transforms emerging technologies into operational capabilities supporting mission-critical customers. This isn't a research-only position. You'll take Generative AI concepts from proof of concept to production, designing, developing, deploying, and continuously improving AI-powered applications that solve real-world operational problems. If you enjoy building with the latest LLMs, experimenting with emerging AI technologies, and deploying secure cloud-native solutions in Azure, we'd like to meet you.
What You'll Do
  • Design and build Generative AI applications using commercial and open-source Large Language Models (LLMs).
  • Develop proof-of-concepts that evolve into production-ready software.
  • Architect Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise data sources.
  • Build intelligent agents and AI workflows capable of reasoning, automation, and decision support.
  • Develop REST APIs and backend services supporting AI applications.
  • Deploy scalable AI solutions within Microsoft Azure environments.
  • Design cloud-native architectures using Azure AI Services, Azure OpenAI, Azure Kubernetes Service (AKS), Azure Functions, and Azure Storage.
  • Build CI/CD pipelines supporting rapid AI deployment and model iteration.
  • Integrate AI capabilities into existing enterprise applications and mission systems.
  • Evaluate emerging AI technologies and rapidly prototype new capabilities.
  • Collaborate with software engineers, cloud architects, data scientists, and mission stakeholders to deliver innovative solutions.
  • Optimize model performance, latency, scalability, security, and cost.
  • Implement responsible AI practices, prompt engineering strategies, guardrails, and model evaluation techniques.

Requirements
  • Active Top Secret Clearance
  • 5+ years of software development experience.
  • 2+ years developing Generative AI or Machine Learning applications.
  • Strong Python development experience.
  • Experience working with Large Language Models including GPT, Llama, Claude, Mistral, or similar models.
  • Experience with prompt engineering and AI workflow development.
  • Experience building APIs using FastAPI, Flask, or similar frameworks.
  • Experience deploying cloud-native applications in Microsoft Azure.
  • Familiarity with containerization technologies including Docker and Kubernetes.
  • Experience with Git, CI/CD pipelines, and DevSecOps practices.
  • Strong understanding of software architecture and distributed systems.
Preferred Qualifications
  • Experience with Azure OpenAI Service.
  • Experience building Retrieval-Augmented Generation (RAG) systems.
  • Knowledge of LangChain, LangGraph, Semantic Kernel, LlamaIndex, or similar orchestration frameworks.
  • Experience with vector databases such as Pinecone, Milvus, pgvector, Azure AI Search, or Chroma.
  • Experience developing AI agents and autonomous workflows.
  • Familiarity with MCP (Model Context Protocol) and agent interoperability concepts.
  • Experience with model evaluation, observability, and prompt optimization.
  • Experience deploying AI solutions in secure or classified environments.
  • Familiarity with Infrastructure as Code using Terraform or Bicep.
  • Knowledge of Azure Machine Learning, Azure AI Foundry, or Azure Cognitive Services.

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