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Embedded Ai Engineer Jobs in Virginia (NOW HIRING)

Booz Allen Hamilton

Computer Vision AI Engineer

Mclean, VA · On-site

$99K - $225K/yr

Experience with embedded systems programming in C, C++, or Rust * Experience in GPU programming ... Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the ...

RIT Solutions

Embedded Software Engineer

Reston, VA · On-site

$136.80K - $180K/yr

Embedded Software Engineer, Intelligence Systems A cutting-edge defense technology company is ... Their family of systems is powered by an AI-driven operating system that integrates thousands of ...

AnaVation LLC

Sr. Cyber Engineer (AI)

Chantilly, VA · On-site

$107K - $146.90K/yr

Senior AI Engineer Be Challenged and Make a Difference In a world of technology, people make the ... Experience analyzing embedded systems, mobile platforms, or cloud-native workloads. * Publications ...

AnaVation LLC

Sr. Cyber Engineer (AI)

Chantilly, VA · On-site

They are seeking a Senior AI Engineer with deep experience in vulnerability research and reverse ... embedded systems, mobile platforms, or cloud-native workloads • Publications, conference ...

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All JobsEmbedded Ai Engineer JobsEmbedded Ai Engineer Jobs in Virginia

Embedded Ai Engineer information

See Virginia salary details

$69.4K

$152.1K

$172.5K

How much do embedded ai engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for embedded ai engineer in Virginia is $152,068.00, according to ZipRecruiter salary data. Most workers in this role earn between $130,400.00 and $171,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Embedded AI Engineer, and why are they important?

To thrive as an Embedded AI Engineer, you need expertise in embedded systems, AI/ML algorithms, programming languages like C/C++ and Python, and typically a degree in computer engineering or a related field. Familiarity with development tools such as TensorFlow Lite, ONNX, embedded Linux, and microcontroller platforms is essential, along with experience deploying AI models on resource-constrained devices. Strong problem-solving, collaboration, and communication skills help you work effectively in multidisciplinary teams and address real-world challenges. These skills ensure efficient integration of AI into embedded systems, enabling innovative, high-performance solutions for edge computing.

How does an Embedded AI Engineer typically collaborate with hardware and software teams during a project?

Embedded AI Engineers work closely with both hardware and software teams to ensure AI models are efficiently integrated into resource-constrained devices. They often collaborate with hardware engineers to optimize model performance based on device limitations like memory and processing power. At the same time, they coordinate with software developers to design efficient firmware and manage data pipelines. Regular cross-functional meetings and code reviews are common to address integration challenges and maintain alignment throughout the project lifecycle.

What is an Embedded AI Engineer?

An Embedded AI Engineer is a professional who designs, develops, and implements artificial intelligence (AI) algorithms and models directly onto embedded systems, such as microcontrollers or edge devices. Their work involves optimizing AI solutions to run efficiently on hardware with limited computing resources, power, and memory. They collaborate with hardware engineers and software developers to integrate machine learning, computer vision, or other AI functionalities into products like smart appliances, autonomous vehicles, or IoT devices. Their expertise helps bring intelligent features directly to devices, enabling real-time decision-making without needing constant cloud connectivity.

What is the difference between Embedded Ai Engineer vs Machine Learning Engineer?

CriteriaEmbedded Ai EngineerMachine Learning Engineer
Required CredentialsBachelor's in Electrical Engineering, Computer Science, or related; knowledge of embedded systemsBachelor's or Master's in Computer Science, Data Science, or related; strong programming skills
Work EnvironmentEmbedded systems, IoT devices, hardware integrationData centers, cloud platforms, software development environments
Employer & Industry UsageConsumer electronics, automotive, IoT companiesTech firms, startups, research institutions
Common Search & ComparisonYesNo

Embedded Ai Engineers focus on integrating AI algorithms into embedded hardware and IoT devices, requiring knowledge of hardware constraints and embedded programming. Machine Learning Engineers develop models primarily for software applications and data analysis. While both roles involve AI, Embedded Ai Engineers specialize in hardware-software integration within embedded systems, whereas Machine Learning Engineers work on developing and deploying AI models in software environments.

What are popular job titles related to Embedded Ai Engineer jobs in Virginia? For Embedded Ai Engineer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Embedded Ai Engineer jobs in Virginia look for? The top searched job categories for Embedded Ai Engineer jobs in Virginia are:
What cities in Virginia are hiring for Embedded Ai Engineer jobs? Cities in Virginia with the most Embedded Ai Engineer job openings:
Embedded Ai Engineer jobs near you
AI Engineer _ GitHub Copilot Agents (Salesforce Development & Testing)

AI Engineer _ GitHub Copilot Agents (Salesforce Development & Testing)

ClifyX

Mclean, VA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

AI Engineer with strong hands-on experience in building GitHub Copilot-based AI agents to accelerate Salesforce development and testing. The role will focus on leveraging agentic AI and prompt engineering to automate SDLC activities and improve delivery efficiency.
Job Description
Must Have Technical/Functional Skills

  • Hands-on experience with GitHub Copilot for development and testing automation
  • Proven ability to build AI agents / Copilot-driven workflows
  • Strong Salesforce development experience (Apex, LWC, integrations)
  • Strong Python programming skills (mandatory)
  • Experience in Prompt Engineering and prompt optimization
  • Experience working with LLMs (GPT, Claude, etc.)
  • Knowledge of:
    • Test automation frameworks
    • SDLC lifecycle automation
    • API and integration patterns
Preferred Skills
  • Experience implementing agentic development in Salesforce ecosystem
  • Exposure to:
    • Agentforce / Salesforce AI / enterprise AI tools
    • CI/CD pipelines with AI automation
  • Experience building:
    • Multi-agent workflows
    • Autonomous testing solutions

Roles & Responsibilities
  1. Copilot Agent Development (Core Responsibility)
    • Design and build AI agents using GitHub Copilot to support:
      • Salesforce development (Apex, LWC, integrations)
      • Automated testing and QA workflows
    • Enable Copilot-driven code generation, refactoring, and debugging across SDLC
  2. Salesforce Development Enablement
    • Leverage AI and Copilot to accelerate:
      • Apex / LWC development
      • API integrations and backend logic
      • Code modernization and enhancements
    • Convert design artifacts (e.g., Figma inputs where applicable) into code scaffolding and components (based on Copilot usage patterns observed in internal PoCs)
  3. AI-Driven Testing & Automation
    • Build AI agents to automate:
      • Test case generation
      • Unit test creation and execution
      • Regression and functional testing
    • Use Copilot to improve test coverage, reduce manual effort, and enable TDD adoption
    • Support transition toward AI-assisted/autonomous testing pipelines
  4. Prompt Engineering & Optimization
    • Design and optimize prompts using:
      • Few-shot, structured prompting techniques
    • Build prompt templates and reusable patterns for development and testing use cases
    • Continuously refine prompts for better accuracy and context relevance
  5. Python & AI Integration
    • Develop backend services and automation scripts using Python
    • Integrate AI agents with:
      • Salesforce platforms
      • DevOps pipelines
      • APIs and enterprise applications
  6. LLM & Agentic Frameworks
    • Implement solutions using LLMs (GPT, Claude, or similar)
    • Build AI workflows using:
      • RAG (Retrieval-Augmented Generation)
      • Frameworks like LangChain (or equivalent)
  7. Collaboration & Delivery
    • Work closely with Salesforce teams, QA, and architects
    • Participate in sprint delivery with embedded AI workflows
    • Drive adoption of AI-led SDLC transformation
ClifyX logo

About ClifyX

Sourced by ZipRecruiter

ClifyX is a well-established player in the IT Services sector that specializes in providing result-oriented technological solutions to a wide range of industrial verticals. Based in South Plainfield, New Jersey, ClifyX offers a comprehensive selection of IT services that include project staffing, application development, professional consulting, and other IT-based solutions. While the company's website, clifyx.com, does not divulge the exact founding date, it is clear that ClifyX has grown into a renowned name within their domain, thanks to their unwavering commitment to innovative practices. The company's mission statement revolves around harnessing the power of technology to assist their clientele in steering their respective businesses towards success.

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

South Plainfield, NJ, US

Year founded

1998

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